mlsanityData to 14 Oct 2026

Recognition in Terra Incognita

arXiv 1807.04975 ↗First posted 13 Jul 2018cs.CV q-bio.PEcited by 658

Not on the map. Why?

Cited by 23 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems.

Key facts. Cited by 658 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jul 2018.

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Citing papers per year: 2021: 102; 2022: 129; 2023: 132; 2024: 107; 2025: 106; 2026: 82.

658 papers.

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  1. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
  2. 2021Domain Generalization: A Surveycited by 764
  3. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 434
  4. 2022Last Layer Re-Training is Sufficient for Robustness to Spurious Correlationscited by 381
  5. 2021Gradient Matching for Domain Generalizationcited by 258
  6. 2021Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalizationcited by 220
  7. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 216
  8. 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 211
  9. 2021Towards Causal Representation Learningcited by 209
  10. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  11. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  12. 2021Partial success in closing the gap between human and machine visioncited by 185
38 more
  1. 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 172
  2. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  3. 2022Handling Distribution Shifts on Graphs: An Invariance Perspectivecited by 164
  4. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  5. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  6. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  7. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  8. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 130
  9. 2021Goal Misgeneralization in Deep Reinforcement Learningcited by 123
  10. 2022SuS-X: Training-Free Name-Only Transfer of Vision-Language Modelscited by 119
  11. 2021Does Invariant Risk Minimization Capture Invariance?cited by 116
  12. 2021Can contrastive learning avoid shortcut solutions?cited by 114
  13. 2021Salient ImageNet: How to discover spurious features in Deep Learning?cited by 114
  14. 2021Counterfactual Generative Networkscited by 111
  15. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 106
  16. 2024Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factorscited by 106
  17. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  18. 2022Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalizationcited by 97
  19. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  20. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  21. 2021When Does Contrastive Visual Representation Learning Work?cited by 92
  22. 2022Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimationcited by 88
  23. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  24. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  25. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  26. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 86
  27. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  28. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  29. 2021Leveraging Sparse Linear Layers for Debuggable Deep Networkscited by 82
  30. 2023Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblockscited by 78
  31. 2021The iWildCam 2021 Competition Datasetcited by 77
  32. 2021Let there be a clock on the beach: Reducing Object Hallucination in Image Captioningcited by 76
  33. 2021Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?cited by 75
  34. 2022Agree to Disagree: Diversity through Disagreement for Better Transferabilitycited by 70
  35. 2021On the Impact of Spurious Correlation for Out-of-distribution Detectioncited by 68
  36. 2022Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypescited by 67
  37. 2024Many-Shot In-Context Learning in Multimodal Foundation Modelscited by 67
  38. 2021Adaptive Methods for Real-World Domain Generalizationcited by 66

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