Paper Citation Record · LEDGER

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2504.14432.

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

pith.paper-citation-record.v1
2504.14432 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:01.295207Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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verified fuzzy26
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External citation measurements

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Outbound references

Observation f304bddb-a710-4735-a1f8-5705df4b77c9 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 1

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Observation 6caaec32-146f-4585-9263-2beb1770d82c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task LLaMA: Open and Efficient Foundation Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.102120Z

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Observation 2a3d2a0a-338b-415c-8e6a-80765a7e319b · outbound

This paper cites Stanford alpaca: An instruction-following llama model,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Stanford alpaca: An instruction-following llama model,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.980865Z

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Observation 278e9e95-9dee-48e8-b39d-177465329327 · outbound

This paper cites Gpt-4 technical report,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Gpt-4 technical report,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.966491Z

Source-reported events for the cited work

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Observation 11e441ed-de59-4337-ad8a-b1d44cea5798 · outbound

This paper cites Chatgpt,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Chatgpt,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.952923Z

Source-reported events for the cited work

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Observation ea945b28-4b8d-4a52-83bb-052b7efb4391 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.938007Z

Source-reported events for the cited work

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Observation 5138eca9-1058-4b8b-b10e-dc469b71be32 · outbound

This paper cites Instructblip: Towards general-purpose vision- language models with instruction tuning,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Instructblip: Towards general-purpose vision- language models with instruction tuning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.923310Z

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Observation 58c22334-2aa7-4cf8-8cce-55eaece7e4bc · outbound

This paper cites LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

Reference 8

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Observation 28b26283-eaf9-46c8-935a-18b9b181ab48 · outbound

This paper cites Visual Instruction Tuning.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Visual Instruction Tuning

Reference 9

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Source-reported events for the cited work

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Observation 8851c7de-d104-4564-a291-f29e56dfc6d4 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.135610Z

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Observation 9727e838-16b8-4ad5-b237-1788a8b561ff · outbound

This paper cites Locality and compositionality in zero-shot learning,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Locality and compositionality in zero-shot learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.908050Z

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Observation 69adc038-f660-44f9-b7f4-27ad234afac9 · outbound

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

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Learning transferable visual models from natural language supervision,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.891547Z

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Observation 53acd390-7574-4ba7-b50f-d3af457f750e · outbound

This paper cites Valley: Video Assistant with Large Language model Enhanced abilitY.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Valley: Video Assistant with Large Language model Enhanced abilitY

Reference 13

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Observation 62ff7709-975a-40ee-8763-a6574ccff3b1 · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.151813Z

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Observation 056694e6-9ce4-442f-9bfa-a5f452209657 · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 15

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Source-reported events for the cited work

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Observation 0a4cadb3-24cc-453a-8095-56a4ab63a266 · outbound

This paper cites VideoChat: Chat-Centric Video Understanding.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task VideoChat: Chat-Centric Video Understanding

Reference 16

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Observation 03ea62b9-90c1-4cb6-8c93-f6389d52ae93 · outbound

This paper cites MovieChat: From Dense Token to Sparse Memory for Long Video Understanding.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.164655Z

Source-reported events for the cited work

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Observation f254d20a-cbfa-475c-891b-cab57b1d8224 · outbound

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

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Learning spatiotemporal features with 3d convolutional networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.875686Z

Source-reported events for the cited work

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Observation 3e539da7-a84f-433b-a24a-2d6638754125 · outbound

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

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.173628Z

Source-reported events for the cited work

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Observation 37d98bee-ba20-498c-9bd2-637a8cb93ba7 · outbound

This paper cites Joint learning of attended zero-shot features and visual-semantic mapping,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Joint learning of attended zero-shot features and visual-semantic mapping,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.853032Z

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Observation 20177b18-fcaa-47fa-9e5d-44a34325ea9c · outbound

This paper cites A Review of Generalized Zero-Shot Learning Methods.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task A Review of Generalized Zero-Shot Learning Methods

Reference 21

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Observation 89429e84-b8f8-4fef-98a3-303e0fb326eb · outbound

This paper cites Learning a deep embedding model for zero-shot learning,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Learning a deep embedding model for zero-shot learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.838532Z

Source-reported events for the cited work

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Observation 75935a25-512b-4e67-b893-7fbb54d1b0c1 · outbound

This paper cites Video question answering via gradually refined attention over appearance and motion,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Video question answering via gradually refined attention over appearance and motion,

Reference 23

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Observation bb6fc5cb-8e23-4633-ba85-5cd65e039f23 · outbound

This paper cites Tgif-qa: Toward spatio- temporal reasoning in visual question answering,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Tgif-qa: Toward spatio- temporal reasoning in visual question answering,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.814518Z

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Observation 7b1f0db8-c479-4411-be56-cef475623f71 · outbound

This paper cites Activitynet- qa: A dataset for understanding complex web videos via question answering,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Activitynet- qa: A dataset for understanding complex web videos via question answering,

Reference 25

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Source-reported events for the cited work

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Observation 7656832f-b8f0-424e-924a-f9683f859924 · outbound

This paper cites Latent dirichlet allocation,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Latent dirichlet allocation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.791105Z

Source-reported events for the cited work

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Observation f78adb90-cac7-48a7-bdfb-27a7dd9b51ac · outbound

This paper cites Efficient estimation of word representations in vector space,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Efficient estimation of word representations in vector space,

Reference 27

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Observation 93315135-3f80-4e4e-b595-389b4aede8e1 · outbound

This paper cites Skip-thought vectors,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Skip-thought vectors,

Reference 28

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Observation 38ce0715-2bf0-48d1-bfe7-6d0469f846b7 · outbound

This paper cites Distributed representations of sentences and documents,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Distributed representations of sentences and documents,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.750315Z

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Observation ff98a2ae-8787-494f-a374-ab359de1a45e · outbound

This paper cites A neural proba- bilistic language model,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task A neural proba- bilistic language model,

Reference 30

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Observation 0c37b4dd-802a-489e-a702-936c6219f618 · outbound

This paper cites Language Models are Few-Shot Learners.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Language Models are Few-Shot Learners

Reference 31

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Observation d05b9459-8a23-4a8a-bee2-7e4da5c94e42 · outbound

This paper cites Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects

Reference 32

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Observation d39ae4f1-3396-4ae7-b745-98df744e60bc · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task OPT: Open Pre-trained Transformer Language Models

Reference 33

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Observation 70888efd-2d1a-42eb-b88f-26df1aec08d8 · outbound

This paper cites Flamingo: A visual language model for few-shot learning,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Flamingo: A visual language model for few-shot learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.721945Z

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Observation b99605de-16e8-49a2-8595-5a15dac97fdf · outbound

This paper cites Towards general purpose vision systems: An end-to-end task-agnostic vision-language architecture,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Towards general purpose vision systems: An end-to-end task-agnostic vision-language architecture,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.708291Z

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Observation 1318fc6c-a02a-4ad8-893f-e3974fc55ec3 · outbound

This paper cites Class-agnostic object detection with multi-modal transformer,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Class-agnostic object detection with multi-modal transformer,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.694600Z

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Observation ad11aa57-caf0-4881-8015-e5d4f645d8a4 · outbound

This paper cites Bridg- ing the gap between object and image-level representations for open- vocabulary detection,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Bridg- ing the gap between object and image-level representations for open- vocabulary detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.679889Z

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Observation c466c31f-8346-4db8-8666-49e04d9a59b0 · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Open-vocabulary semantic segmentation with mask-adapted clip,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.666490Z

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Observation 83e32dad-0985-4a08-afed-7c6f3a8c4d07 · outbound

This paper cites Language-grounded indoor 3d semantic segmentation in the wild,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Language-grounded indoor 3d semantic segmentation in the wild,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.652301Z

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Observation bdd060f2-07d9-4739-9535-d14d05633eaf · outbound

This paper cites Expanding language-image pretrained models for general video recognition,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Expanding language-image pretrained models for general video recognition,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.637665Z

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Observation ec746aca-0426-400e-9372-d412337a601a · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task ActionCLIP: A New Paradigm for Video Action Recognition

Reference 41

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Observation a46ef225-0370-40f3-8308-8689c32b7d34 · outbound

This paper cites Finetuned clip models are efficient video learners,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Finetuned clip models are efficient video learners,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.622589Z

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Observation 8417e37e-6722-40b1-9afb-9a3f9743a26e · outbound

This paper cites Deep residual learning for image recognition,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Deep residual learning for image recognition,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.278352Z

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Observation e0eebc19-9393-4a68-9cad-bba3fedf1478 · outbound

This paper cites Temporal segment networks: Towards good practices for deep action recognition,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Temporal segment networks: Towards good practices for deep action recognition,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T11:53:01.282427Z

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Observation 0604e75d-9422-4794-ab3b-cc9e8cf058a7 · outbound

This paper cites Activi- tynet: A large-scale video benchmark for human activity understanding,.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Activi- tynet: A large-scale video benchmark for human activity understanding,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.590115Z

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Observation 0ba76b65-e53d-49a9-8a97-f5deaed3158c · outbound

This paper cites BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning.

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning

Reference 46

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Observation c0097f47-a2fb-420e-999e-701f5f012d1a · outbound

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

ResNetVLLM -- Multi-modal Vision LLM for the Video Understanding Task Zero-shot video question answering via frozen bidirectional language models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:53:01.575169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.