mlsanityData to 14 Oct 2026

TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection

arXiv 2412.05292 ↗First posted 22 Nov 2024cs.CV cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (`anchors') from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at urlhttps://github.com/Cverchen/TagFog.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 22 Nov 2024.

Builds on

35 of 47 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  5. 2013Describing Textures in the Wildcited by 2,125
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  7. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  8. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  9. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  10. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  11. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  12. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
23 more
  1. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  2. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  3. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  4. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  5. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  6. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  7. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  8. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  9. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  10. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  11. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 252
  12. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  13. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 216
  14. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  15. 2017Generative OpenMax for Multi-Class Open Set Classificationcited by 157
  16. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  17. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 152
  18. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  19. 2019Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancycited by 112
  20. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
  21. 2021Can multi-label classification networks know what they don't know?cited by 100
  22. 2022Training OOD Detectors in their Natural Habitatscited by 86
  23. 2022Back to the Basics: Revisiting Out-of-Distribution Detection Baselinescited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2025Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Surveycited by 6
  2. 2025Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detectioncited by 4
  3. 2025BOOD: Boundary-based Out-Of-Distribution Data Generationcited by 4
  4. 2026TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Modelscited by 2
  5. 2026A text-guided vision model for enhanced recognition of small instancescited by 1
  6. 2025Local Background Features Matter in Out-of-Distribution Detectioncited by 0
  7. 2026CHILD: Human-in-the-Loop OOD Detection for Safe Clinical Deploymentcited by 0
  8. 2026Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Bettingcited by 0