Multi-Modal Dataset Distillation in the Wild | Paper Citation Record

Observation e8861af3-38f8-4c25-8f19-aaaef357860e · outbound

This paper cites Unsupervised label noise modeling and loss correction.

Multi-Modal Dataset Distillation in the Wild Unsupervised label noise modeling and loss correction

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.165600Z

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Observation efdd2078-e862-4310-a29b-2c6a3ce63018 · outbound

This paper cites A closer look at memorization in deep networks.

Multi-Modal Dataset Distillation in the Wild A closer look at memorization in deep networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.157870Z

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Observation 7f8ab341-63db-4ae0-b198-ab39fcf739fc · outbound

This paper cites High-performance large-scale image recognition without normalization.

Multi-Modal Dataset Distillation in the Wild High-performance large-scale image recognition without normalization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.598573Z

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Observation 854c2aee-f540-4ebb-8ce5-2e11a65c7e81 · outbound

This paper cites Dataset distillation by matching training trajectories.

Multi-Modal Dataset Distillation in the Wild Dataset distillation by matching training trajectories

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.147325Z

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Observation f07af8d5-6464-446c-ad7c-4a45ebac25f8 · outbound

This paper cites Scaling up dataset distillation to imagenet- 1k with constant memory.

Multi-Modal Dataset Distillation in the Wild Scaling up dataset distillation to imagenet- 1k with constant memory

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.604742Z

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Observation b87bd022-4fd0-405c-b2c2-9e6da6d39564 · outbound

This paper cites Noisy correspondence learning with self-reinforcing errors mitigation.

Multi-Modal Dataset Distillation in the Wild Noisy correspondence learning with self-reinforcing errors mitigation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.136589Z

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Observation 0bd327be-de2a-45cd-9146-8080f28289e5 · outbound

This paper cites Disentangled noisy correspondence learning.IEEE Transactions on Image Processing, 2025.

Multi-Modal Dataset Distillation in the Wild Disentangled noisy correspondence learning.IEEE Transactions on Image Processing, 2025

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.128806Z

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Observation 148b4a67-7ba3-4c88-b5c2-9cc8b33efb25 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Multi-Modal Dataset Distillation in the Wild Imagenet: A large- scale hierarchical image database

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.614319Z

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Observation 146cf841-c440-457c-9c11-5dd6f40bf66f · outbound

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

Multi-Modal Dataset Distillation in the Wild BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.617842Z

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Observation 99a4ebda-862c-4ca9-8610-eefde61ba500 · outbound

This paper cites Similarity reasoning and filtration for image-text matching.

Multi-Modal Dataset Distillation in the Wild Similarity reasoning and filtration for image-text matching

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.117914Z

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Observation 73963fd7-8ffd-4acc-b711-86f66c01f52c · outbound

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

Multi-Modal Dataset Distillation in the Wild An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.623896Z

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Observation ffd70ee0-e1fc-4c4c-b309-4b82a44d1585 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

Multi-Modal Dataset Distillation in the Wild Robust loss functions under label noise for deep neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.627268Z

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Observation 00b6ced3-5327-409a-9f16-f3ea46173565 · outbound

This paper cites To- wards lossless dataset distillation via difficulty-aligned trajectory matching.

Multi-Modal Dataset Distillation in the Wild To- wards lossless dataset distillation via difficulty-aligned trajectory matching

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.105134Z

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Observation b60c3f33-9746-4cd1-9794-fa014abba173 · outbound

This paper cites Noisy correspondence learning with meta similarity correction.

Multi-Modal Dataset Distillation in the Wild Noisy correspondence learning with meta similarity correction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.097434Z

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Observation c1ea639d-6560-42cc-89d6-b7bc6e0e3e29 · outbound

This paper cites Deep residual learning for image recognition.

Multi-Modal Dataset Distillation in the Wild Deep residual learning for image recognition

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.636081Z

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Observation 6380d337-a121-4206-81aa-73690bef5e05 · outbound

This paper cites Learning with noisy correspondence for cross-modal matching.Advances in Neural Information Processing Systems, 34:29406–29419, 2021.

Multi-Modal Dataset Distillation in the Wild Learning with noisy correspondence for cross-modal matching.Advances in Neural Information Processing Systems, 34:29406–29419, 2021

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.084662Z

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Observation e94a469b-3ef5-403d-8b76-3fd81866bb1e · outbound

This paper cites GPT-4o System Card.

Multi-Modal Dataset Distillation in the Wild GPT-4o System Card

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.642536Z

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Observation 356208d9-5248-429f-b550-9ac5fed69c36 · outbound

This paper cites Generating action-conditioned prompts for open-vocabulary video action recognition.

Multi-Modal Dataset Distillation in the Wild Generating action-conditioned prompts for open-vocabulary video action recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.077429Z

Source-reported events for the cited work

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Observation cb979748-d402-44de-a642-7f6d58eacc77 · outbound

This paper cites AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant.

Multi-Modal Dataset Distillation in the Wild AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.648911Z

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Observation e94d00c8-0c40-4a7a-8949-ce13bb988b5f · outbound

This paper cites ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting.

Multi-Modal Dataset Distillation in the Wild ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.652221Z

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Observation 237c2e77-f764-4350-a308-218f9e4474ed · outbound

This paper cites Stacked cross attention for image-text matching.

Multi-Modal Dataset Distillation in the Wild Stacked cross attention for image-text matching

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.069553Z

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Observation 9cdf1e19-db08-4b94-a30f-c3b055432a5b · outbound

This paper cites Factorized contrastive learning: Going beyond multi-view redundancy.Advances in Neural Information Processing Systems, 36:32971–32998, 2023.

Multi-Modal Dataset Distillation in the Wild Factorized contrastive learning: Going beyond multi-view redundancy.Advances in Neural Information Processing Systems, 36:32971–32998, 2023

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.658496Z

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Observation 25d1c708-a111-4614-afc0-7bdfbeac7eeb · outbound

This paper cites Focal Loss for Dense Object Detection.

Multi-Modal Dataset Distillation in the Wild Focal Loss for Dense Object Detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.661485Z

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Observation 939c7d9f-00ca-4975-a2d1-de1224246c22 · outbound

This paper cites Microsoft coco: Common objects in context.

Multi-Modal Dataset Distillation in the Wild Microsoft coco: Common objects in context

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.664971Z

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Observation 375f0d62-a06b-461e-a33a-751344175ddf · outbound

This paper cites Energy-based out-of-distribution detection.Advances in neural information processing systems, 33:21464–21475, 2020.

Multi-Modal Dataset Distillation in the Wild Energy-based out-of-distribution detection.Advances in neural information processing systems, 33:21464–21475, 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.667770Z

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Observation 7685f918-5206-4b40-96cd-ed3c9a50a328 · outbound

This paper cites Dataset distillation with convexi- fied implicit gradients.

Multi-Modal Dataset Distillation in the Wild Dataset distillation with convexi- fied implicit gradients

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.049680Z

Source-reported events for the cited work

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Observation 8ca428d1-6a3d-44d2-9994-3a9cc63990d2 · outbound

This paper cites The expectation-maximization algorithm.IEEE Signal processing magazine, 13(6):47–60, 1996.

Multi-Modal Dataset Distillation in the Wild The expectation-maximization algorithm.IEEE Signal processing magazine, 13(6):47–60, 1996

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.674164Z

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Observation d38af957-41f6-41c0-a47d-c3b7c59aa6d2 · outbound

This paper cites Dataset meta-learning from kernel ridge-regression.

Multi-Modal Dataset Distillation in the Wild Dataset meta-learning from kernel ridge-regression

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.037629Z

Source-reported events for the cited work

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Observation e6c10140-769c-4e7e-81db-c340c2e91b38 · outbound

This paper cites Autogps: Automated geometry problem solving via multimodal formalization and deductive reasoning.

Multi-Modal Dataset Distillation in the Wild Autogps: Automated geometry problem solving via multimodal formalization and deductive reasoning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.681017Z

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Observation 1a37ad62-6a50-4cb4-9ebf-cb93c254c207 · outbound

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

Multi-Modal Dataset Distillation in the Wild Learning transferable visual models from natural language supervision

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.684439Z

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Observation 3f3301c7-802e-4621-8ff9-2c7d881f976d · outbound

This paper cites Design- ing network design spaces.

Multi-Modal Dataset Distillation in the Wild Design- ing network design spaces

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.687851Z

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Observation 8fa11317-b950-4750-b38c-cc5ea2593d65 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Multi-Modal Dataset Distillation in the Wild DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.690958Z

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Observation 0b83ff28-56d0-4595-ad73-ac782ea2d3de · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Multi-Modal Dataset Distillation in the Wild Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.694200Z

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Observation 41c371e5-c526-4741-9f46-962f4f052643 · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach.

Multi-Modal Dataset Distillation in the Wild Active learning for convolutional neural networks: A core-set approach

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.697298Z

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Observation b2067827-0103-4213-acbd-e676f0eae0cd · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning.

Multi-Modal Dataset Distillation in the Wild Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.700698Z

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Observation 09a004e0-f92a-41db-8d12-4e104d54bb13 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Multi-Modal Dataset Distillation in the Wild Gemini: A Family of Highly Capable Multimodal Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.704007Z

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Observation fc1b7df1-950e-449b-8fb5-8b96e6c4ee84 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Multi-Modal Dataset Distillation in the Wild An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.707363Z

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Observation 0e3b9f3c-8145-4ae8-8909-143a6515b9d2 · outbound

This paper cites High-frequency component helps explain the generalization of convolutional neural networks.

Multi-Modal Dataset Distillation in the Wild High-frequency component helps explain the generalization of convolutional neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.010178Z

Source-reported events for the cited work

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Observation 75781d3d-d7da-4628-90ce-3e346f96d578 · outbound

This paper cites Cafe: Learning to condense dataset by aligning features.

Multi-Modal Dataset Distillation in the Wild Cafe: Learning to condense dataset by aligning features

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:18.002261Z

Source-reported events for the cited work

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Observation aeb995fd-8f1e-457d-96e3-22f7eabc67c6 · outbound

This paper cites Vision-Language Dataset Distillation.

Multi-Modal Dataset Distillation in the Wild Vision-Language Dataset Distillation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.715799Z

Source-reported events for the cited work

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Observation 624b556a-045f-4eb8-ab6f-967d60547aa4 · outbound

This paper cites OS-ATLAS: A Foundation Action Model for Generalist GUI Agents.

Multi-Modal Dataset Distillation in the Wild OS-ATLAS: A Foundation Action Model for Generalist GUI Agents

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.719167Z

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Observation f0075f6f-ef8a-4208-a1a5-b160590e188d · outbound

This paper cites Regularly truncated m-estimators for learning with noisy labels.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

Multi-Modal Dataset Distillation in the Wild Regularly truncated m-estimators for learning with noisy labels.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.993304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 697a923b-7797-415e-9ea9-3250b58ff5bf · outbound

This paper cites Low-rank similarity mining for multimodal dataset distillation.

Multi-Modal Dataset Distillation in the Wild Low-rank similarity mining for multimodal dataset distillation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.985434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72f127ff-b64f-4ae9-9ea6-d5fa71e75d52 · outbound

This paper cites Bicro: Noisy correspondence rectification for multi-modality data via bi-directional cross-modal similarity consistency.

Multi-Modal Dataset Distillation in the Wild Bicro: Noisy correspondence rectification for multi-modality data via bi-directional cross-modal similarity consistency

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.976962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b90443d6-955a-4163-aa62-ab460dc0388f · outbound

This paper cites Robust noisy correspondence learning with equivariant similarity consistency.

Multi-Modal Dataset Distillation in the Wild Robust noisy correspondence learning with equivariant similarity consistency

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.730548Z

Source-reported events for the cited work

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Observation 535f2fda-a9dd-4561-b547-edd701223930 · outbound

This paper cites Searching to exploit memorization effect in learning with noisy labels.

Multi-Modal Dataset Distillation in the Wild Searching to exploit memorization effect in learning with noisy labels

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.963044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 54bfabab-ea71-44be-a768-6d842048d54d · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.

Multi-Modal Dataset Distillation in the Wild From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.736950Z

Source-reported events for the cited work

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Observation ffbc2e0a-137e-4e71-96bb-e85f1faa6974 · outbound

This paper cites Dataset distillation: A comprehensive review.

Multi-Modal Dataset Distillation in the Wild Dataset distillation: A comprehensive review

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.950045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ef2287a9-f7f7-4a71-9e98-420a2dbc2052 · outbound

This paper cites Visualizing and understanding convolutional networks.

Multi-Modal Dataset Distillation in the Wild Visualizing and understanding convolutional networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.941323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b8cd8f3-fb65-4c97-b3cb-483bf72bf782 · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural information processing systems, 31, 2018.

Multi-Modal Dataset Distillation in the Wild Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural information processing systems, 31, 2018

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.746008Z

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Observation 6ec1e89c-c0d5-4527-90f1-5afa4a9fb78a · outbound

This paper cites Dataset condensation with gradient matching.

Multi-Modal Dataset Distillation in the Wild Dataset condensation with gradient matching

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:44:17.748861Z

Source-reported events for the cited work

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Observation bfd47396-edcb-4a88-aa03-a651d55b9f15 · outbound

This paper cites Mitigating noisy correspondence by geometrical structure consistency learning.

Multi-Modal Dataset Distillation in the Wild Mitigating noisy correspondence by geometrical structure consistency learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:44:17.924828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e769d190-4e96-4786-a79b-6e6f31f96fc0 · outbound

This paper cites girl" and “couple.

Multi-Modal Dataset Distillation in the Wild girl" and “couple

Reference 53

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T11:44:17.915653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Pith citing papers