mlsanityData to 14 Oct 2026

AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains

arXiv 2212.14397 ↗First posted 29 Dec 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

In addition to impressive performance, vision transformers have demonstrated remarkable abilities to encode information they were not trained to extract. For example, this information can be used to perform segmentation or single-view depth estimation even though the networks were only trained for image recognition. We show that a similar phenomenon occurs when explicitly training transformers for semantic segmentation in a supervised manner for a set of categories: Once trained, they provide valuable information even about categories absent from the training set. This information can be used to segment objects from these never-seen-before classes in domains as varied as road obstacles, aircraft parked at a terminal, lunar rocks, and maritime hazards.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 29 Dec 2022.

Builds on

33 of 48 references.

References · 33

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  4. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  5. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  7. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  8. 2021Vision Transformers for Dense Predictioncited by 1,803
  9. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  10. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
  11. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  12. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,152
21 more
  1. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  2. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  3. 2021Localizing Objects with Self-Supervised Transformers and no Labelscited by 230
  4. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  5. 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
  6. 2018Evaluating Bayesian Deep Learning Methods for Semantic Segmentationcited by 143
  7. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  8. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  9. 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
  10. 2021OW-DETR: Open-world Detection Transformercited by 129
  11. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  12. 2022TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cutcited by 113
  13. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  14. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  15. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  16. 2022DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognitioncited by 42
  17. 2020Dense open-set recognition with synthetic outliers generated by Real NVPcited by 29
  18. 2020Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentationcited by 20
  19. 2021Dense outlier detection and open-set recognition based on training with noisy negative imagescited by 17
  20. 2021Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Datacited by 10
  21. 2022Perspective Aware Road Obstacle Detectioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Beyond the Global Scores: Fine-Grained Token Grounding as a Robust Detector of LVLM Hallucinationscited by 3
  2. 2026R'enyi Attention Entropy for Patch Pruningcited by 0
  3. 2026ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detectioncited by 0
  4. 2026Adversarial Decoys: Misdirecting Attention-Based Defenses in ViTcited by 0