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Paper Citation Record · LEDGER

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining

As of 19 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 0 inbound Pith citation observations for arXiv:2505.06557.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.06557 v1

Coverage vector

measured 96 of 96 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:43:20.048571Z

measured 96 of 96 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

96 of 96 outbound references displayed

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  • verified fuzzy63
  • unresolved33
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 2a9fdea8-0644-4bc1-8d2e-32e8503a0ac4 · outbound

This paper cites Video summarization with long short-term memory.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video summarization with long short-term memory

Reference 1

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Observation c05ac608-05ea-4735-88f1-254b0cdf2558 · outbound

This paper cites Video summarization using fully convolutional sequence networks.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video summarization using fully convolutional sequence networks

Reference 2

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Observation 4d120700-fc96-47ba-a985-5a256550041e · outbound

This paper cites Egoexolearn: A dataset for bridging asynchronous ego-and exo-centric view of procedural activities in real world.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Egoexolearn: A dataset for bridging asynchronous ego-and exo-centric view of procedural activities in real world

Reference 3

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Observation 35846bb6-ba86-4488-8f19-9b54e29614c1 · outbound

This paper cites Dual encoding for zero-example video retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Dual encoding for zero-example video retrieval

Reference 4

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Observation 07ecbc59-1a0f-45a2-bad2-cb180c89f465 · outbound

This paper cites Multi-modal transformer for video retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Multi-modal transformer for video retrieval

Reference 5

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Observation 33341392-df84-4359-8312-d7a377208b82 · outbound

This paper cites T2vlad: global-local sequence alignment for text-video retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining T2vlad: global-local sequence alignment for text-video retrieval

Reference 6

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Observation 71d90c8f-530d-4724-a7b6-adff54b75829 · outbound

This paper cites Mutual context network for jointly estimating egocentric gaze and action.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Mutual context network for jointly estimating egocentric gaze and action

Reference 7

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Observation bc2c775f-3add-445a-9a74-13d5542488ba · outbound

This paper cites Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video

Reference 8

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source=pdf_text observed=2026-08-15T22:43:19.719784Z digest=sha256:e15e97943677d59b92c555177846f0e131c82d3acb21bfc72f0b9eba66f153d7

Observation 8a8ed187-fde2-4636-93ec-75ad04689dfa · outbound

This paper cites Predicting gaze in egocentric video by learning task-dependent attention transition.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Predicting gaze in egocentric video by learning task-dependent attention transition

Reference 9

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Observation 0e48ebce-c4a5-4922-b4e7-ed0e05a60e1e · outbound

This paper cites Tall: Temporal activity localization via language query.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Tall: Temporal activity localization via language query

Reference 10

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Observation 0ee991dc-6a84-40cc-8022-2b4dcde1d7ad · outbound

This paper cites Boundary proposal network for two-stage natural language video localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Boundary proposal network for two-stage natural language video localization

Reference 11

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Observation 70d86978-1349-4460-b51a-dd6d13160a54 · outbound

This paper cites Univtg: Towards unified video-language temporal grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Univtg: Towards unified video-language temporal grounding

Reference 12

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Observation f6e61de2-ada2-48f6-911b-619bd954951b · outbound

This paper cites Mac: Mining activity concepts for language-based temporal localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Mac: Mining activity concepts for language-based temporal localization

Reference 13

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Observation 2f25237b-3baa-4f9a-8529-a801c8f57767 · outbound

This paper cites Learning 2d temporal adjacent networks for moment localization with natural language.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Learning 2d temporal adjacent networks for moment localization with natural language

Reference 14

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Observation 32ff957e-6821-40fd-97ee-f85ce53572c1 · outbound

This paper cites Progressive localization networks for language-based moment localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Progressive localization networks for language-based moment localization

Reference 15

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Observation 4ecf493a-3856-43b1-9086-2cb30f44e491 · outbound

This paper cites Weakly supervised temporal sentence grounding with gaussian- based contrastive proposal learning.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised temporal sentence grounding with gaussian- based contrastive proposal learning

Reference 16

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Observation b3bb69ad-c247-4516-9079-c7518e1d6d31 · outbound

This paper cites Gaussian mixture proposals with pull-push learning scheme to capture diverse events for weakly supervised temporal video grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Gaussian mixture proposals with pull-push learning scheme to capture diverse events for weakly supervised temporal video grounding

Reference 17

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Observation 5475a2b6-6672-4d9f-ad59-250d54cae501 · outbound

This paper cites WSLLN: Weakly Supervised Natural Language Localization Networks.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining WSLLN: Weakly Supervised Natural Language Localization Networks

Reference 18

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Observation cf592482-5e5c-40d2-b72e-f50be6c7ac10 · outbound

This paper cites Cross-sentence temporal and semantic relations in video activity localisation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Cross-sentence temporal and semantic relations in video activity localisation

Reference 19

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 11e99359-6844-4444-9d07-df0780581741 · outbound

This paper cites Logan: Latent graph co-attention network for weakly-supervised video moment IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 12 retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Logan: Latent graph co-attention network for weakly-supervised video moment IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 12 retrieval

Reference 20

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Observation 887434f3-6da3-42a7-b3a3-9348de38bed7 · outbound

This paper cites Weakly- supervised video moment retrieval via semantic completion network.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly- supervised video moment retrieval via semantic completion network

Reference 21

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Observation 4a5f22f0-2612-4fe9-af5e-57c557b409d3 · outbound

This paper cites Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos

Reference 22

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Observation ce00389e-6be6-43fc-b61e-c08e6de72593 · outbound

This paper cites Weakly supervised video moment localization with contrastive negative sample mining.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised video moment localization with contrastive negative sample mining

Reference 23

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Observation b9e5f90f-3c82-404e-9ec0-b7055b55abd5 · outbound

This paper cites Localizing moments in video with natural language.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Localizing moments in video with natural language

Reference 24

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Observation 6fa55ff5-edf3-44fe-838b-1e72b855d29e · outbound

This paper cites Cross-modal video moment retrieval with spatial and language-temporal attention.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Cross-modal video moment retrieval with spatial and language-temporal attention

Reference 25

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Observation bbeffb93-af23-4426-a607-bb3ea489681a · outbound

This paper cites Temporally grounding natural sentence in video.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Temporally grounding natural sentence in video

Reference 26

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Observation c9cfb7ea-2d00-4a4c-89ee-345c2ffb2432 · outbound

This paper cites Semantic conditioned dynamic modulation for temporal sentence grounding in videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Semantic conditioned dynamic modulation for temporal sentence grounding in videos

Reference 27

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 344336e1-5ee5-4d92-a2a4-2a36a24c19c2 · outbound

This paper cites Multi-modal interaction graph convolutional network for temporal language localization in videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Multi-modal interaction graph convolutional network for temporal language localization in videos

Reference 28

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 03d9e6cc-e400-4ea9-adba-327af52a7bf4 · outbound

This paper cites Thinking inside uncertainty: Interest moment perception for diverse temporal grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Thinking inside uncertainty: Interest moment perception for diverse temporal grounding

Reference 29

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source=pdf_text observed=2026-08-15T22:43:19.795890Z digest=sha256:d61cacb9503216ba933c39c22b1636093f9041c631194240dce72f9130410ba5

Observation e5d0b2ab-b697-44ef-91ed-a88f6f37a26b · outbound

This paper cites Camg: Context-aware moment graph network for multimodal temporal activity localization via language.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Camg: Context-aware moment graph network for multimodal temporal activity localization via language

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.799519Z digest=sha256:e726dba23169ff1bc7fa9f0715809ccbc1e21857b58eaabde2f9d512418246b0

Observation d290e369-e395-4b38-9621-3fa7071497d7 · outbound

This paper cites Video moment retrieval via comprehensive relation-aware network.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video moment retrieval via comprehensive relation-aware network

Reference 31

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.803192Z digest=sha256:15fc31b7e6b1d75b44df6a7cafd3ccc7e99148fc47f824c89a9f906c3f97945c

Observation ebc3d1f6-cc3a-4b66-b52a-cfff9377cf98 · outbound

This paper cites Video corpus moment retrieval via deformable multi granularity feature fusion and adversarial training.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video corpus moment retrieval via deformable multi granularity feature fusion and adversarial training

Reference 32

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.807376Z digest=sha256:0272735a68f8ab74dae871e2d9bbbf918a7652f0a585b23326a68c027f120bec

Observation 4ce6beaa-8cf9-4b61-beb7-addf784d2155 · outbound

This paper cites Collaborative debias strategy for temporal sentence grounding in video.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Collaborative debias strategy for temporal sentence grounding in video

Reference 33

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.811384Z digest=sha256:69e5d2ed37bbf3d35776e7652338c828cc687888c4a62cdf1a7e34663cbe9366

Observation 896f8d0b-3a49-434d-9160-48697c0d7100 · outbound

This paper cites Uncovering Hidden Challenges in Query-Based Video Moment Retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Uncovering Hidden Challenges in Query-Based Video Moment Retrieval

Reference 34

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source=pdf_text observed=2026-08-15T22:43:19.814983Z digest=sha256:11f036333c81aa0dd95cbdb3ce931de13002871648185a92d0e016831b5efb0b

Observation 335ccf54-5aaf-45e7-a30a-b8d74f9a8989 · outbound

This paper cites Cola: Weakly-supervised temporal action localization with snippet contrastive learning.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Cola: Weakly-supervised temporal action localization with snippet contrastive learning

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.819016Z digest=sha256:a1b9e35768548eef837b360abcc21b001b1341d69be14344223e633cf7e505a3

Observation 219d6b14-5a4d-4ca5-a7fd-aed636c38a00 · outbound

This paper cites Exploring sub-action granularity for weakly supervised temporal action localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Exploring sub-action granularity for weakly supervised temporal action localization

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.822400Z digest=sha256:c4e875856f94fdd95f0610f16793ab654cbc7274449d716e2927a26e005f9444

Observation 7e6eee0c-d984-44b8-b241-d284f89533a0 · outbound

This paper cites Learning proposal-aware re-ranking for weakly- supervised temporal action localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Learning proposal-aware re-ranking for weakly- supervised temporal action localization

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.759859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.826341Z digest=sha256:700aa0c0eada4f73ff201242adc423d04d807d7023534bd9db960267e3095ce6

Observation 7de543a6-0d46-4872-aa8b-5709f3f809e6 · outbound

This paper cites Cross-video contextual knowledge exploration and exploitation for ambiguity reduction in weakly supervised temporal action localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Cross-video contextual knowledge exploration and exploitation for ambiguity reduction in weakly supervised temporal action localization

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.748692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.829861Z digest=sha256:dd5f502915fca38dae9f882c67d2093fc1a77bc0fa7f0ad82bcd21b3c52c5d0f

Observation 2b6a5e24-7010-41ae-b950-774ff40aa032 · outbound

This paper cites A snippets relation and hard-snippets mask network for weakly-supervised temporal action localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining A snippets relation and hard-snippets mask network for weakly-supervised temporal action localization

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.737697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.833458Z digest=sha256:79260e81d4042459bdfb2c1c7642dab49c5d58b3920378868f4444ed5be4a115

Observation 1cb512e6-a782-41c7-a0da-5e3f28c2c53c · outbound

This paper cites Solve the puzzle of instance segmentation in videos: A weakly supervised framework with spatio-temporal collaboration.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Solve the puzzle of instance segmentation in videos: A weakly supervised framework with spatio-temporal collaboration

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.725294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.837009Z digest=sha256:f23832303f8c4cb5359dbdc4a9bb287acbb579d5266cc734ab0b86d31c9e116d

Observation 3264f0a9-18fb-4ca2-8e1e-925f9c04857a · outbound

This paper cites Weakly supervised video instance segmentation with scale adaptive generation regulation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised video instance segmentation with scale adaptive generation regulation

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.713711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.840752Z digest=sha256:4b2e564eb8b3dded87a4c4ad58809afcdb04b14433310cfdd6116c8c8099380f

Observation eb918e2b-6e61-42fa-aa97-0db1e0b18c57 · outbound

This paper cites Towards video anomaly detection in the real world: A binarization embedded weakly-supervised network.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Towards video anomaly detection in the real world: A binarization embedded weakly-supervised network

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.702749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.844340Z digest=sha256:fc2f943aaf062c6c686edad0c67c365d040c28551be9eab5b9b7fbe92ac8ae90

Observation 618b355a-8eaa-4bb6-b6ab-e7efbdf99f2f · outbound

This paper cites Vadclip: Adapting vision-language models for weakly supervised video anomaly detection.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Vadclip: Adapting vision-language models for weakly supervised video anomaly detection

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.691956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.848230Z digest=sha256:668ef2dd7f636960106bb7ef9d13c3858695f68836cc4bf9148c8f29671fe0d6

Observation 24b2707b-175d-4009-baa5-a7e9fbd11bde · outbound

This paper cites Batchnorm-based weakly supervised video anomaly detection.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Batchnorm-based weakly supervised video anomaly detection

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.680217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.851807Z digest=sha256:a2cd4a64ada57d366a8252d14ac8ae25deabe58a8be36f1a50b167d2681a4856

Observation 15994ed0-25ca-4868-acd2-f4d5bf5daf42 · outbound

This paper cites Weakly supervised video moment retrieval from text queries.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised video moment retrieval from text queries

Reference 45

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unresolved
no resolver link, observed 2026-08-15T22:43:19.855241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:19.855241Z digest=sha256:cecc635076fad4f5c557c5d2fd78009a39e852182d38febaf14d9df3e9af7307

Observation 3440bc77-641c-4965-9d3e-3c44bfa2f263 · outbound

This paper cites Vlanet: Video-language alignment network for weakly-supervised video moment retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Vlanet: Video-language alignment network for weakly-supervised video moment retrieval

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.662653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.858766Z digest=sha256:e3f70a35f666dfafcaee49c2c45600076e6d5d4134795a873d8235656195a93c

Observation 8a9781a7-b394-4fcc-9eb6-7bb26042e84d · outbound

This paper cites Weakly supervised temporal sentence grounding with uncertainty-guided self-training.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised temporal sentence grounding with uncertainty-guided self-training

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.652170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.862370Z digest=sha256:3d74abd7bb94944ec7a4142aff9eb63201e550c8ff33987c3bc4276438dac7d2

Observation ffc53d2e-3bb1-421c-99b2-3d13087738f2 · outbound

This paper cites Unsupervised hard example mining from videos for improved object detection.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Unsupervised hard example mining from videos for improved object detection

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.641437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.865706Z digest=sha256:72bf0084782c88b85f1b0a5cafb27f7abe7e014937e650e14197278d839ccfe8

Observation 89858af6-af6b-4750-be96-209ab2b3420e · outbound

This paper cites Contrastive Learning with Hard Negative Samples.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Contrastive Learning with Hard Negative Samples

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:19.869228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:19.869228Z digest=sha256:92fd60b026fa261ef1c9e655253196a8d68ed26eabf507c0c7e578188f53cdea

Observation 7a9c0299-9025-4c8a-8176-6320b97c8900 · outbound

This paper cites Video corpus moment retrieval with contrastive learning.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video corpus moment retrieval with contrastive learning

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.630037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.873249Z digest=sha256:f9ea58d2a297d1928c2143128db34f037f75fe206d8522c71c7ad0152f2e0953

Observation 149d7d89-edf5-4191-8ae0-107deb44e623 · outbound

This paper cites Selective query-guided debiasing for video corpus moment retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Selective query-guided debiasing for video corpus moment retrieval

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.619524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.877108Z digest=sha256:28b4e0fb068b7fbe77dee24527e9cff92c53179cf0132f715ded144687ca6c85

Observation 5da81a9e-69c9-4376-a8d5-1eeb64f58e8f · outbound

This paper cites Counterfactual two-stage debiasing for video corpus moment retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Counterfactual two-stage debiasing for video corpus moment retrieval

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.608778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.881003Z digest=sha256:d09c8a8b56ddcdc0257beb9406b3a58bb0ab14c804fa967220b93ff57c6c2945

Observation fe083306-2953-4d21-bc54-b4cf8c68f876 · outbound

This paper cites Curriculum multi-negative augmentation for debiased video grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Curriculum multi-negative augmentation for debiased video grounding

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.597175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.884681Z digest=sha256:ab1afe0624bb8868ebc7645c3c80729b07a46eaa6c9ac3e396cfa691c96d4cd8

Observation f08840e4-c1d1-4857-8ee2-39d8d74310d0 · outbound

This paper cites Bias-conflict sample synthesis and adversarial removal debias strategy for temporal sentence grounding in video.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Bias-conflict sample synthesis and adversarial removal debias strategy for temporal sentence grounding in video

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.586147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.888249Z digest=sha256:a02b6028c0bf33c8e3e0c1cfeac8e3ac3af8298cb8996d3e2e03290582b290ec

Observation e2c8e9d6-8405-4c3c-ac99-5bafd66f98f4 · outbound

This paper cites Learnable negative proposals using dual-signed cross-entropy loss for weakly supervised video moment localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Learnable negative proposals using dual-signed cross-entropy loss for weakly supervised video moment localization

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.575041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.892238Z digest=sha256:864f549b3804cad623294ae6818ccd5adcc3d5ad6519256a88908db86494aeaf

Observation 970a30a0-baee-4f3d-b879-c5fb7fcbf4ef · outbound

This paper cites Contextual similarity distillation for asymmetric image retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Contextual similarity distillation for asymmetric image retrieval

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.564459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.896038Z digest=sha256:d05ee97727f4666c319ea37b60aa5b2038d5827c84fa5eb753cc717f00e0337e

Observation eb33f60b-9589-4f2a-96dc-8228a97bc9e2 · outbound

This paper cites Ames: Asymmetric and memory-efficient similarity estimation for instance-level retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Ames: Asymmetric and memory-efficient similarity estimation for instance-level retrieval

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.553662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.899583Z digest=sha256:b4f874b9916a8041146228e6da8c6eba418eed6f2fe722b467ced21cd48debb9

Observation 48b0ec08-019c-410a-b8e9-77499a9cf2b2 · outbound

This paper cites D3still: Decoupled differential distillation for IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 13 asymmetric image retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining D3still: Decoupled differential distillation for IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 13 asymmetric image retrieval

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.541067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.903096Z digest=sha256:3117089cdadf92cb5318882a9562267362a0eb6cee79f242da39df70f24ffd18

Observation 4ac88705-f153-422e-bf6a-82bf7247e4e8 · outbound

This paper cites Domain adaptation by constraining inter-domain variability of latent feature representation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Domain adaptation by constraining inter-domain variability of latent feature representation

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.529923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.906898Z digest=sha256:a7099db600ee6b8539f7031db709a30fe38e8c6ef0dc8e8e7b34c0b1c943adbd

Observation 2878e0a0-7691-4098-9b04-b04e529d13e7 · outbound

This paper cites Exploiting inter-sample affinity for knowability-aware universal domain adaptation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Exploiting inter-sample affinity for knowability-aware universal domain adaptation

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.518463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.910733Z digest=sha256:4591a5a5cd641dd00fb8288005289a12991de9d935fa9bee944afce267e1728f

Observation a840d690-32ec-43a0-bd48-9c6909e6b3b7 · outbound

This paper cites Mining inter-video proposal relations for video object detection.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Mining inter-video proposal relations for video object detection

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.506595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.914440Z digest=sha256:da03e02bad254d61994166e3cd5e1d0033ffd600cab60fa811aeb95a9f0b426a

Observation bb8bf427-649d-430d-85f6-a1fc1e2e7fe2 · outbound

This paper cites Imc- det: Intra–inter modality contrastive learning for video object detection.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Imc- det: Intra–inter modality contrastive learning for video object detection

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.494911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.918055Z digest=sha256:459c2bc9dd890ebd0f9930b23405fb30cf68fd7faec72f526ea58919493a8c9b

Observation 5a705531-fb85-41f1-a9c7-95b2b0d9a686 · outbound

This paper cites Attention is all you need.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Attention is all you need

Reference 63

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unresolved
no resolver link, observed 2026-08-15T22:43:19.921774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:19.921774Z digest=sha256:39f336f9323ca999ca5f521ca9a7a207e207962f2e9b51cb20da67705b67229b

Observation 34d91ec7-8161-433a-bc3e-7e5a1b606598 · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Learning spatiotemporal features with 3d convolutional networks

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:19.925344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:19.925344Z digest=sha256:9aaba0c06281e18abf0ec7c01f95e5def3c029b9aad64074035cfe6bb6017ac0

Observation 643042b8-3a5d-499b-849d-f1e13cc517bb · outbound

This paper cites Glove: Global vectors for word representation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Glove: Global vectors for word representation

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.468032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.928843Z digest=sha256:47844af4ca6568da3021b7ca57c30d624d8de0c8129575ba3e49ff1da46c4387

Observation 325488be-9b22-4eb5-aba9-10356ae214d4 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:19.932548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:19.932548Z digest=sha256:4c94f705a25c646aa724e96e0d4faff823d9e77ab2bb4b39a2e67817b5daa686

Observation d6b2202e-7151-401d-83f8-781a14b6c0d1 · outbound

This paper cites Dense-captioning events in videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Dense-captioning events in videos

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.448977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.936257Z digest=sha256:3ea8ccfe4f9da05999c97600716b44dcaa45326ef1a7fc2c1fd0606ad692f276

Observation 4b5f6e7b-f8e4-4956-b22e-f48d609f706f · outbound

This paper cites Can i trust your answer? visually grounded video question answering.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Can i trust your answer? visually grounded video question answering

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.438350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.940024Z digest=sha256:2c081c41257a9a878492a09f0616c30183c4a6b9ab15247339dc63a7af064135

Observation 0304d4f1-a88a-4c41-b91f-667ca9014b85 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Quo vadis, action recognition? a new model and the kinetics dataset

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.427536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.943546Z digest=sha256:65c257f906e478e6dd164d351a039a3660300de8d0cd81e810929720c29b41af

Observation c3e7adf4-078f-4dd3-a472-f685746e80cf · outbound

This paper cites Revisiting the" video" in video-language understanding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Revisiting the" video" in video-language understanding

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.416645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.947174Z digest=sha256:dfee31dc8fdac060e6f341be23ed38eab0e3b20ad244691055162d4621023351

Observation 6b411558-8d9e-4ac8-9fb0-66be5580f1c4 · outbound

This paper cites Zero-shot video question answering via frozen bidirectional language models.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Zero-shot video question answering via frozen bidirectional language models

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.405017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.950847Z digest=sha256:31a1f904180db0372737ff2f6767a6bc0c87bc8e602366915f580ac79ff80d16

Observation b7bee19f-a9d6-4415-bcd6-9887b201c1bc · outbound

This paper cites Weakly supervised temporal adjacent network for language grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Weakly supervised temporal adjacent network for language grounding

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.393475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.954466Z digest=sha256:62ce1870f8e61c21249358e707f906235f2be69894e57d5960869629e6f6c4bc

Observation 96918027-900f-49c0-bf59-eb40fc637cd9 · outbound

This paper cites Reinforcement learning for weakly supervised temporal grounding of natural language in untrimmed videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Reinforcement learning for weakly supervised temporal grounding of natural language in untrimmed videos

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.382337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.958346Z digest=sha256:5a70e5a43353ff01507794549db27641a83e199172a4bbc355630330f9276b02

Observation d581b098-0427-451f-ac73-107b7df7c901 · outbound

This paper cites Counterfactual contrastive learning for weakly-supervised vision-language grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Counterfactual contrastive learning for weakly-supervised vision-language grounding

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.369993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.962187Z digest=sha256:64b2273c2f294003344d2f85ce30d2caafd0e5a02778f179dec8cff8f7460915

Observation 54067aa4-07b7-4443-a898-8e85e1064ab5 · outbound

This paper cites Regularized two-branch proposal networks for weakly-supervised moment retrieval in videos.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Regularized two-branch proposal networks for weakly-supervised moment retrieval in videos

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.358746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.966570Z digest=sha256:7be07a1445dcfa4cee398b06aeaa6311b577a25ac0fd4edb132e91587a153f11

Observation abe4be8a-3fa3-4bcb-855e-bc106c07f576 · outbound

This paper cites Visual co-occurrence alignment learning for weakly-supervised video moment retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Visual co-occurrence alignment learning for weakly-supervised video moment retrieval

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.345920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.971211Z digest=sha256:b87c02d137f0d824d69015d77d0d3af71ebce2d3b0f355068e9bb043b5ba085a

Observation 7935f0c9-5e6c-4147-95c0-963d1cfbb8a1 · outbound

This paper cites Local correspondence network for weakly supervised temporal sentence grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Local correspondence network for weakly supervised temporal sentence grounding

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.333119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.974812Z digest=sha256:c40674d2f6d72f09357ffef0e9449141ff653b9f504766de76fc667988cfd953

Observation ad9a5107-7ac0-4f5a-b405-d74d293f050b · outbound

This paper cites Explore inter- contrast between videos via composition for weakly supervised temporal sentence grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Explore inter- contrast between videos via composition for weakly supervised temporal sentence grounding

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.320700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.978468Z digest=sha256:1e6fa8f7af5022fd1df19abf973823b9be5aa68740a7420bd579ff0d990a11e8

Observation ed3e6f22-0b46-490c-8477-eaed0677603e · outbound

This paper cites Dynamic contrastive learning with pseudo-samples intervention for weakly supervised joint video mr and hd.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Dynamic contrastive learning with pseudo-samples intervention for weakly supervised joint video mr and hd

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.306332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.981898Z digest=sha256:054b3c8391f57b381faed6486edc3ef0dec7fa67e354243cfa07bd968f5fd9c3

Observation 21b5e737-1f67-4cec-a1b8-42a8e461281b · outbound

This paper cites Counterfactual cross-modality reasoning for weakly supervised video moment localization.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Counterfactual cross-modality reasoning for weakly supervised video moment localization

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.294687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.985503Z digest=sha256:64b2f7151170d2a9606be53caaf45358be947dad3206466f9c66fcb83582fca7

Observation d4e82e30-c6b2-4890-95ce-4f5cb67f8131 · outbound

This paper cites Scanet: Scene complexity aware network for weakly-supervised video moment retrieval.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Scanet: Scene complexity aware network for weakly-supervised video moment retrieval

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.283182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.988989Z digest=sha256:4c76b9fb05be0650f783846735891247cfeb949795aa98610e1f7ea24ae8f72e

Observation 28fde601-d8f7-4fbc-a635-2f75245b1381 · outbound

This paper cites Omnipotent distillation with llms for weakly- supervised natural language video localization: When divergence meets consistency.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Omnipotent distillation with llms for weakly- supervised natural language video localization: When divergence meets consistency

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.271532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.992618Z digest=sha256:a03d244bc1ee088a57982efb91ccfa8eb7bee36d1d1a12dfce0797cecf882d4c

Observation 7908f722-4948-4fc5-80f2-eb6493d73e45 · outbound

This paper cites Local-global multi-modal distillation for weakly-supervised temporal video grounding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Local-global multi-modal distillation for weakly-supervised temporal video grounding

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.260219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:19.997464Z digest=sha256:a96943ccfd116b860a2f9cd00b70a538a631f632fd4e5bb3aacb085a269e25d3

Observation 05623014-2435-4f94-93ee-da20808d0205 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Swin transformer: Hierarchical vision transformer using shifted windows

Reference 84

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.001159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.001159Z digest=sha256:63145dbea80676420124df0f76c167b0b9715243519a53d1ce7a6b656496493f

Observation 686ce1de-e499-49bb-974e-2da524ae188a · outbound

This paper cites Learning transferable visual models from natural language supervision.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Learning transferable visual models from natural language supervision

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.004978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.004978Z digest=sha256:01ddfbf3d4b44e5a757e6fe60468357532db031d4aa3f45a63d25cfd70544fe5

Observation 416b530e-22d5-46ff-87bf-9439998500ae · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 86

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.008815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.008815Z digest=sha256:f3b5118e44ce00d5119ff75475dc67e4f625ac84b0c9130dee5105ba8444c9f3

Observation a4cfb65f-3895-4343-9edd-552fffb88ec2 · outbound

This paper cites Gpt-4 technical report.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Gpt-4 technical report

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.012289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.012289Z digest=sha256:8f0a9f538128f9e421ce22665eac47fbcd8e091c30c2e4c6c8648486ff6c46e9

Observation b24a125d-84da-472d-90fb-ac47ef9235fd · outbound

This paper cites Video graph transformer for video question answering.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video graph transformer for video question answering

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.220452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:20.015759Z digest=sha256:03e711210d15568c1b3b8ec4e06257e2b59e77918368be6350041e7e6941d8dc

Observation ab7800b2-1a36-4d0e-b8ad-0f70589be518 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Rich feature hierarchies for accurate object detection and semantic segmentation

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.208954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:20.019077Z digest=sha256:98d35eb33bf2b569ca87d61d5119c8d066e08d90f3ce03f730ef9bb8dad1c0e1

Observation a310cc85-523b-415a-8fe0-70d718011f08 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.023267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.023267Z digest=sha256:72806f83243a8f23803e4f613c02ff8218c61a7d4addb950bb88a0cf2e7770e7

Observation 23f2386d-ec4e-4133-8838-d27ee202579c · outbound

This paper cites An empirical study of end-to-end video- language transformers with masked visual modeling.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining An empirical study of end-to-end video- language transformers with masked visual modeling

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.197067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:20.028863Z digest=sha256:479d76d09749ab525a7de3b596ab6005c8135250a821fc193025a80171889077

Observation 487f9db1-acfd-49e1-b854-d87899b7d05a · outbound

This paper cites Video swin transformer.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Video swin transformer

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.183931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:20.032674Z digest=sha256:99432e51af9e2b2165c6cc29b5229311e47191a40d1bcf78b790e8ee75a741de

Observation 46d8acbe-9cf4-415d-8536-cce0ddccb539 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.036070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.036070Z digest=sha256:d596396cc0e0d2730adbdcb06c7eb47c7fb4983d32338d98386a494089e1c075

Observation e1de9da1-8a41-4052-acd5-8b5cc605d58d · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.039918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.039918Z digest=sha256:b9d2fdb5902b6fca3ef753758f91522550ee25edb213aece262959be3d3ca347

Observation f3c689d7-8bc2-4a67-a0f7-7d6ec7e8cf44 · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-15T22:43:20.043878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:43:20.043878Z digest=sha256:2a30c55ea7a5805a81ae9ad4309f558aae8be9956373f75fdbf677f7c0840d8b

Observation d2458d1d-7c91-42b1-bc92-2181972fac9a · outbound

This paper cites Visualizing data using t-sne.

Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining Visualizing data using t-sne

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:43:20.169778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-15T22:43:20.048571Z digest=sha256:72471d56c5d95b113b23ec0166edc3eea942e2c5bb62d3650fb3db4b596d5223

Pith citing papers

No inbound Pith citation observations are available.