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DETR ECCV20
- 1. From Transformer to Detection: End-to-End Object Detection with Transformers Presented by Frost IVUL@KAUST
- 2. Content • What is DETR? • DETR pipeline • Background: Transformer • Transformer in DETR • Set-Based global loss • Main Results • Conclusion • Ideas • References
- 3. What is DETR? • Approaches object detection as a direct set prediction problem. • Transformer encoder-decoder architecture. • Set-based global loss: forces unique predictions via bipartite matching. • Due to this parallel nature, DETR is very fast and efficient. (in testing time)
- 5. Background: Transformer • Sequence to Sequence • Encoder-Decoder
- 6. Background: Transformer • Sequence to Sequence • Encoder-Decoder • Encoder: Self-Attention + FFN
- 8. Background: Transformer • Sequence to Sequence • Encoder-Decoder • Encoder: Self-Attention + FFN • Decoder: Self-Attention + Encoder-Decoder Attention + FFN
- 10. Background: Transformer • Sequence to Sequence • Encoder-Decoder • Encoder: Self-Attention + FFN • Decoder: ... + E-D Att + ... • Multi-Headed Attention
- 11. Background: Transformer • Sequence to Sequence • Encoder-Decoder • Encoder: Self-Attention + FFN • Decoder: ... + E-D Att + ... • Multi-Headed Attention • Positional Encoding
- 12. Background: Transformer • Sequence to Sequence • Encoder-Decoder • Encoder: Self-Attention + FFN • Decoder: ... + E-D Att + ... • Multi-Headed Attention • Positional Encoding • That’s it!
- 13. Content • What is DETR? • DETR pipeline • Background: Transformer • Transformer in DETR • Set-Based global loss • Main Results • Conclusion • Ideas • References
- 14. Back to DETR
- 15. Transformer in DETR 1. positional encodings passed at every attention layer 2. queries are initially set to zero
- 16. Transformer in DETR 1. positional encodings passed at every attention layer 2. queries are initially set to zero
- 17. Set-based global loss Maximize iou
- 18. Set-based global loss a permutation of N elements predictions Maximize iou bipartite matching
- 19. Set-based global loss a permutation of N elements predictions Maximize iou
- 20. Content • What is DETR? • DETR pipeline • Background: Transformer • Transformer in DETR • Set-Based global loss • Main Results • Conclusion • Ideas • References
- 21. Main Results
- 22. Conclusion • A fresh design for object detection systems based on transformers and bipartite matching loss for direct set prediction. • Significantly better performance on large objects than Faster R-CNN, • Global information performed by the self-attention. • It under performs on a smaller objects compared to other object detectors of same magnitude. • It takes long training hours and is not real time. • The transformer architecture leads to significant overhead in training/inference
- 23. Ideas • Detection in 3D • Temporal Action Localization • Segmentation / Super-resolution • ... • ... • ... • ... • ...
- 24. References • DETR Paper • http://jalammar.github.io/illustrated-transformer/ • https://ai.facebook.com/blog/end-to-end-object-detection-with- transformers/ • https://medium.com/inside-machine-learning/what-is-a-transformer- d07dd1fbec04 • https://www.geeksforgeeks.org/maximum-bipartite-matching/ • https://medium.com/visionwizard/detr-b677c7016a47
Notas del editor
- 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.