SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks | Paper Citation Record

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This paper cites http://www.gphoto.com/.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks http://www.gphoto.com/

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This paper cites On sensor bias in experimental methods for comparing interest-point, saliency, and recognition algorithms.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks On sensor bias in experimental methods for comparing interest-point, saliency, and recognition algorithms

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This paper cites Why do deep convolutional networks generalize so poorly to small image transformations?.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Why do deep convolutional networks generalize so poorly to small image transformations?

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This paper cites Unexplored faces of robustness and out-of-distribution: Covariate shifts in environment and sensor domains.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Unexplored faces of robustness and out-of-distribution: Covariate shifts in environment and sensor domains

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

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This paper cites From colouring-in to pointillism: revisiting semantic segmentation supervision.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks From colouring-in to pointillism: revisiting semantic segmentation supervision

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Are we done with ImageNet?

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks PaliGemma: A versatile 3B VLM for transfer

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This paper cites A digital image processing pipeline for modelling of realistic noise in synthetic images.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A digital image processing pipeline for modelling of realistic noise in synthetic images

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This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks YOLOv4: Optimal Speed and Accuracy of Object Detection

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks COYO-700M: Image-text pair dataset

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks End-to-end object detection with Transformers

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks On the Generalization Mystery in Deep Learning

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks How is Gaze Influenced by Image Transformations? Dataset and Model

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Benchmarking robustness of adaptation methods on pre-trained vision-language models

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks PaLI: A jointly-scaled multilingual language-image model

Reference 17

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Reproducible scaling laws for contrastive language-image learning

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks ImageNet: A large-scale hierarchical image database

Reference 19

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This paper cites Understanding how image quality affects deep neural networks.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Understanding how image quality affects deep neural networks

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This paper cites A study and comparison of human and deep learning recognition performance under visual distortions.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A study and comparison of human and deep learning recognition performance under visual distortions

Reference 21

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks An image is worth 16x16 words: Transformers for image recognition at scale

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks In search of robust measures of generalization

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks The Pascal Visual Object Classes (VOC) Challenge

Reference 24

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Data filtering networks

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks YOLOv3: An Incremental Improvement

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Shortcut learning in deep neural networks

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Generalisation in humans and deep neural networks

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Fast R-CNN

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Truth or backpropaganda? an empirical investigation of deep learning theory

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This paper cites Strengths and weaknesses of deep learning models for face recognition against image degradations.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Strengths and weaknesses of deep learning models for face recognition against image degradations

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Wukong: A 100 million large-scale Chinese cross-modal pre-training benchmark

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks ExifTool

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Recent advances in deep learning theory

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Masked autoencoders are scalable vision learners

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Deep residual learning for image recognition

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Benchmarking neural network robustness to common corruptions and perturbations

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Natural adversarial examples

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Scene recognition with CNNs: Objects, scales and dataset bias

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, 2025

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Visual Genome: Connecting language and vision using crowdsourced dense image annotations

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Robustness and repeatability of saliency models subjected to visual degradations

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Can multiple-choice questions really be useful in detecting the abilities of llms? In LREC-COLING, 2024

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Exploring plain vision transformer backbones for object detection

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks VILA: On pre-training for visual language models

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Microsoft COCO: Common objects in context

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Improved baselines with visual instruction tuning

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Evaluation of LBP and deep texture descriptors with a new robustness benchmark

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks SSD: Single shot multibox detector

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Swin Transformer: Hierarchical vision transformer using shifted windows

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A decade’s battle on dataset bias: Are we there yet? In ICLR, 2025

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A convnet for the 2020s

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks DeepSeek-VL: Towards Real-World Vision-Language Understanding

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This paper cites Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Simple open-vocabulary object detection

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks MNIST-C: A Robustness Benchmark for Computer Vision

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This paper cites Uniform convergence may be unable to explain generalization in deep learning.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Uniform convergence may be unable to explain generalization in deep learning

Reference 58

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This paper cites Evaluation of visual saliency analysis algorithms in noisy images.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Evaluation of visual saliency analysis algorithms in noisy images

Reference 59

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This paper cites Localization recall precision (LRP): A new performance metric for object detection.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Localization recall precision (LRP): A new performance metric for object detection

Reference 60

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This paper cites One metric to measure them all: Localisation recall precision (LRP) for evaluating visual detection tasks.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks One metric to measure them all: Localisation recall precision (LRP) for evaluating visual detection tasks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.887237Z

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This paper cites Im2Text: Describing images using 1 million captioned photographs.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Im2Text: Describing images using 1 million captioned photographs

Reference 62

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This paper cites NMS Strikes Back.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks NMS Strikes Back

Reference 63

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks PsychoPy2: Experiments in behavior made easy

Reference 64

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This paper cites Dataset issues in object recognition.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Dataset issues in object recognition

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.581371Z

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Observation e4e383db-aaf2-42bd-bfcf-e4e0328589ca · outbound

This paper cites Basics Photography 07: Exposure.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Basics Photography 07: Exposure

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.524617Z

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This paper cites Dataset growth.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Dataset growth

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.450882Z

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

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Learning transferable visual models from natural language supervision

Reference 68

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SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Camera exposure determination

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.376115Z

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This paper cites ImageNet-21K Pretraining for the Masses.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks ImageNet-21K Pretraining for the Masses

Reference 70

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This paper cites Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches

Reference 71

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This paper cites Effects of Degradations on Deep Neural Network Architectures.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Effects of Degradations on Deep Neural Network Architectures

Reference 72

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This paper cites LAION-400M: Open dataset of CLIP-filtered 400 million image-text pairs.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks LAION-400M: Open dataset of CLIP-filtered 400 million image-text pairs

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.332125Z

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This paper cites Objects365: A large-scale, high-quality dataset for object detection.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Objects365: A large-scale, high-quality dataset for object detection

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.205607Z

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Observation 2d0a0782-231a-4b46-997f-25ea2148dbf1 · outbound

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

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 75

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This paper cites Assessing visually-continuous corruption robustness of neural networks relative to human performance.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Assessing visually-continuous corruption robustness of neural networks relative to human performance

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.094307Z

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Observation 056ae939-c8ba-4e55-bc43-add5667829f3 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Very deep convolutional networks for large-scale image recognition

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:25.244910Z

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This paper cites WIT: Wikipedia- based image text dataset for multimodal multilingual machine learning.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks WIT: Wikipedia- based image text dataset for multimodal multilingual machine learning

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:31.000899Z

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This paper cites Convnets and ImageNet beyond accuracy: Understanding mistakes and uncovering biases.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Convnets and ImageNet beyond accuracy: Understanding mistakes and uncovering biases

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.877662Z

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Observation 881eaa8d-7e37-4c1c-8b4a-7a58ce644793 · outbound

This paper cites A survey on statistical theory of deep learning: Approximation, training dynamics, and generative models.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A survey on statistical theory of deep learning: Approximation, training dynamics, and generative models

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.773502Z

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Observation da6e972b-1125-4cf3-8e08-3d1c62d1d3ca · outbound

This paper cites YFCC100M: The new data in multimedia research.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks YFCC100M: The new data in multimedia research

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.633096Z

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This paper cites A deeper look at dataset bias.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A deeper look at dataset bias

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.458429Z

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Observation 9b54e344-8bab-4f6b-93cb-6ce3cb914cdc · outbound

This paper cites Unbiased look at dataset bias.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Unbiased look at dataset bias

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.363494Z

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This paper cites A robustness analysis of deep q networks.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks A robustness analysis of deep q networks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.263952Z

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This paper cites From ImageNet to image classification: Contextualizing progress on benchmarks.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks From ImageNet to image classification: Contextualizing progress on benchmarks

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.173180Z

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This paper cites Why does data-driven beat theory-driven computer vision? In ICCVW, 2019.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Why does data-driven beat theory-driven computer vision? In ICCVW, 2019

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:30.054639Z

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This paper cites Probing the Effect of Selection Bias on Generalization: A Thought Experiment.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Probing the Effect of Selection Bias on Generalization: A Thought Experiment

Reference 87

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:19:27.944051Z

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This paper cites CSPNet: A new backbone that can enhance learning capability of CNN.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks CSPNet: A new backbone that can enhance learning capability of CNN

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.947802Z

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Observation 3e95fd5b-16fe-4d36-aa17-ab4035056fbd · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 89

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This paper cites Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:26.317605Z

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This paper cites InternImage: Exploring large-scale vision foundation models with deformable convolutions.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks InternImage: Exploring large-scale vision foundation models with deformable convolutions

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.858990Z

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Observation a02a840d-b72a-407e-af89-5ec714aaa83e · outbound

This paper cites Physical adversarial attack meets computer vision: A decade survey.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Physical adversarial attack meets computer vision: A decade survey

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.734336Z

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Observation 69e7edf9-65f0-4e72-b23f-31a4bffd27ec · outbound

This paper cites Active Control of Camera Parameters for Object Detection Algorithms.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Active Control of Camera Parameters for Object Detection Algorithms

Reference 93

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:19:27.775717Z

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Observation 475974c2-f41e-409d-9dca-43fd7da44412 · outbound

This paper cites Statistic analysis of millions of digital photos.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Statistic analysis of millions of digital photos

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.575010Z

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Observation 94322036-7102-44de-96b0-41ce336efb80 · outbound

This paper cites Statistic analysis of millions of digital photos 2017.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Statistic analysis of millions of digital photos 2017

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.453066Z

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Observation d4629c19-863f-480b-9726-f29c5216ec4a · outbound

This paper cites Does robustness on ImageNet transfer to downstream tasks? In CVPR, 2022.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Does robustness on ImageNet transfer to downstream tasks? In CVPR, 2022

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.345097Z

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Observation 61688f7f-2aa6-43d9-9a18-55e5301e22c4 · outbound

This paper cites Yocto-Light-V3 product page.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Yocto-Light-V3 product page

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.240502Z

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Observation c46afc1e-fd01-4207-9265-8cb60b5641ad · outbound

This paper cites Understanding bias in large-scale visual datasets.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Understanding bias in large-scale visual datasets

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:29.105796Z

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Observation ceb31487-5a14-4cec-af1c-b673c2123afd · outbound

This paper cites Sigmoid loss for language image pre-training.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Sigmoid loss for language image pre-training

Reference 99

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Observation 5d1bd571-986b-4b9d-bfac-c48f7a19c2da · outbound

This paper cites Understanding deep learning (still) requires rethinking generalization.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Understanding deep learning (still) requires rethinking generalization

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:19:28.993090Z

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