mlsanityData to 7 Oct 2026

Can OOD Object Detectors Learn from Foundation Models?

arXiv 2409.05162 ↗First posted 8 Sep 2024cs.CV cs.LGcited by 10

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Abstract (arXiv)

Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such as Stable Diffusion, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples, thereby enhancing OOD object detection. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models to automatically extract meaningful OOD data from text-to-image generative models. This offers the model access to open-world knowledge encapsulated within off-the-shelf foundation models. The synthetic OOD samples are then employed to augment the training of a lightweight, plug-and-play OOD detector, thus effectively optimizing the in-distribution (ID)/OOD decision boundaries. Extensive experiments across multiple benchmarks demonstrate that SyncOOD significantly outperforms existing methods, establishing new state-of-the-art performance with minimal synthetic data usage.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 53 of its 67 references that are in the map. First posted on arXiv 8 Sep 2024.

Builds on

53 of 67 references.

References · 53

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38 more
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50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 4.

10 papers.

  1. 2025Hita: Holistic Tokenizer for Autoregressive Image Generationcited by 11
  2. 2024Learning from Neighbors: Category Extrapolation for Long-Tail Learningcited by 9
  3. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  4. 2025Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detectioncited by 5
  5. 2026AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectorscited by 2
  6. 2025Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?cited by 1
  7. 20252COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Drivingcited by 1
  8. 2026Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learningcited by 0
  9. 2026Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Modelscited by 0
  10. 2026NegAS: Negative Label Guided Attention and Scoring for Out-of-Distribution Object Detection with Vision-Language Modelscited by 0

Similar, not cited

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  1. 2024Detecting Out-of-Distribution Objects through Class-Conditioned Inpainting
  2. 2025A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models
  3. 2025Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
  4. 2025VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection
  5. 2025Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection
  6. 2025Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey
  7. 2025SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Models
  8. 2026Fi-ImageNet-1k: An OOD Benchmark From the Inside of the ImageNet-1k Validation Set