mlsanityData to 7 Oct 2026

End-to-End Object Detection with Transformers

arXiv 2005.12872 ↗First posted 26 May 2020cs.CVcited by 7,384

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Cited by 253 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. Training code and pretrained models are available at https://github.com/facebookresearch/detr.

Key facts. Cited by 7,384 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2020.

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Citing papers per year: 2021: 877; 2022: 1391; 2023: 1642; 2024: 1475; 2025: 1203; 2026: 796.

7,384 papers.

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  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2023Segment Anythingcited by 8,158
  3. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  6. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
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  9. 2022Visual Prompt Tuningcited by 1,674
  10. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  11. 2021ViViT: A Video Vision Transformercited by 1,610
  12. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,610
38 more
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  4. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,490
  5. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  6. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,305
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  10. 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
  11. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  12. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  13. 2021Grounded Language-Image Pre-trainingcited by 1,147
  14. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  15. 2021Perceiver: General Perception with Iterative Attentioncited by 1,089
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  17. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
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  19. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
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  22. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  23. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  24. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  25. 2023A Survey on Multimodal Large Language Modelscited by 898
  26. 2021Multiscale Vision Transformerscited by 896
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  28. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  29. 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesiscited by 854
  30. 2021DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsificationcited by 849
  31. 2023Vision Transformers Need Registerscited by 844
  32. 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
  33. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  34. 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
  35. 2023LISA: Reasoning Segmentation via Large Language Modelcited by 770
  36. 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 767
  37. 2021Domain Generalization: A Surveycited by 760
  38. 2021Going deeper with Image Transformerscited by 759

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