A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters | Paper Citation Record

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

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Flamingo: a visual language model for few-shot learning

Reference 1

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This paper cites Learning from positive and unlabeled data: A survey.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Learning from positive and unlabeled data: A survey

Reference 5

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This paper cites Unsupervised learning of visual features by con- trasting cluster assignments.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Unsupervised learning of visual features by con- trasting cluster assignments

Reference 6

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This paper cites Emerging properties in self-supervised vi- sion transformers.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Emerging properties in self-supervised vi- sion transformers

Reference 7

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This paper cites Chen and K.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Chen and K

Reference 8

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Unresolved cited work

Reference 9

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This paper cites Incremental False Negative Detection for Contrastive Learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Incremental False Negative Detection for Contrastive Learning

Reference 10

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This paper cites Debiased contrastive learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Debiased contrastive learning

Reference 11

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This paper cites Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment

Reference 12

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This paper cites Dwibedi, Y.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Dwibedi, Y

Reference 14

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This paper cites All4one: Symbiotic neighbour contrastive learning via self-attention and re- dundancy reduction.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters All4one: Symbiotic neighbour contrastive learning via self-attention and re- dundancy reduction

Reference 15

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This paper cites SEED: Self-supervised Distillation For Visual Representation.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters SEED: Self-supervised Distillation For Visual Representation

Reference 17

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This paper cites Cloob: Modern hopfield networks with in- foloob outperform clip.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Cloob: Modern hopfield networks with in- foloob outperform clip

Reference 18

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This paper cites Tailoring visual object representations to human requirements: A case study with a recycling robot.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Tailoring visual object representations to human requirements: A case study with a recycling robot

Reference 19

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This paper cites A Review on Discriminative Self-supervised Learning Methods in Computer Vision.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters A Review on Discriminative Self-supervised Learning Methods in Computer Vision

Reference 20

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This paper cites SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters SynCo: Synthetic Hard Negatives for Contrastive Visual Representation Learning

Reference 21

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This paper cites Strub, F.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Strub, F

Reference 22

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This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters A survey on self-supervised learning: Algorithms, applications, and future trends

Reference 23

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This paper cites Audioclip: Extending clip to image, text and audio.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Audioclip: Extending clip to image, text and audio

Reference 24

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This paper cites Momentum contrast for unsu- pervised visual representation learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Momentum contrast for unsu- pervised visual representation learning

Reference 25

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This paper cites Adco: Adversarial contrast for efficient learn- ing of unsupervised representations from self-trained neg- ative adversaries.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Adco: Adversarial contrast for efficient learn- ing of unsupervised representations from self-trained neg- ative adversaries

Reference 26

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This paper cites Boosting contrastive self-supervised learning with false negative cancellation.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Boosting contrastive self-supervised learning with false negative cancellation

Reference 27

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This paper cites A survey on contrastive self-supervised learn- ing.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters A survey on contrastive self-supervised learn- ing

Reference 28

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This paper cites Scaling up visual and vision-language representation learning with noisy text su- pervision.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Scaling up visual and vision-language representation learning with noisy text su- pervision

Reference 29

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This paper cites Kalantidis, M.B.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Kalantidis, M.B

Reference 30

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This paper cites Supervised con- trastive learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Supervised con- trastive learning

Reference 31

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This paper cites Mean shift for self-supervised learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Mean shift for self-supervised learning

Reference 32

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This paper cites Visualbert: A simple and performant baseline for vision and language.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Visualbert: A simple and performant baseline for vision and language

Reference 33

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This paper cites MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

Reference 35

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Audio-visual instance discrimination with cross-modal agreement

Reference 36

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This paper cites Representation Learning with Contrastive Predictive Coding.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Representation Learning with Contrastive Predictive Coding

Reference 37

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This paper cites Focus on the pos- itives: Self-supervised learning for biodiversity monitor- ing.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Focus on the pos- itives: Self-supervised learning for biodiversity monitor- ing

Reference 39

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This paper cites Learning transferable visual models from nat- ural language supervision.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Learning transferable visual models from nat- ural language supervision

Reference 40

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This paper cites Contrastive Learning with Hard Negative Samples.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Contrastive Learning with Hard Negative Samples

Reference 41

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This paper cites Singh, R.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Singh, R

Reference 42

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Hard Negative Sampling Strategies for Contrastive Representation Learning

Reference 43

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This paper cites LXMERT: Learning Cross-Modality Encoder Representations from Transformers.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Reference 44

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This paper cites Active Data Curation Effectively Distills Large-Scale Multimodal Models.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Active Data Curation Effectively Distills Large-Scale Multimodal Models

Reference 45

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This paper cites Oracle-guided Contrastive Clustering.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Oracle-guided Contrastive Clustering

Reference 46

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This paper cites Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 47

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Wav2clip: Learning robust audio representations from clip

Reference 48

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This paper cites Cit: Curation in training for effective vision-language data.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Cit: Curation in training for effective vision-language data

Reference 49

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation

Reference 50

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This paper cites Zbontar, L.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Zbontar, L

Reference 51

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This paper cites Contrastive Learning with Synthetic Positives.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Contrastive Learning with Synthetic Positives

Reference 52

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This paper cites CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning

Reference 53

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This paper cites Panambur, D.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Panambur, D

Reference 2018

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This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 2019

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A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Geography-aware self-supervised learn- ing

Reference 2020

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This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 2021

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This paper cites Self-supervised learning by cross-modal audio- video clustering.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Self-supervised learning by cross-modal audio- video clustering

Reference 2022

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Observation 4438d27e-ed02-4fe2-a7fb-c2020c5219f3 · outbound

This paper cites Data curation via joint example selection further accelerates multimodal learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Data curation via joint example selection further accelerates multimodal learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T10:23:04.427693Z

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Observation c45e8d76-8606-43ac-8587-e7e03e3e0a96 · outbound

This paper cites Synthetic hard negative samples for contrastive learning.

A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters Synthetic hard negative samples for contrastive learning

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T10:23:05.356791Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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