안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2020 CVPR에 발표된 'Detection in Crowded Scenes: One Proposal, Multiple Predictions' 라는 제목의 논문입니다. 오늘의 논문도 제목에서 유추가 가능하듯, 여러 오브젝트가, 특히 중첩이 되어 있는 crowded object Detection을 목적으로 합니다. 뛰어난 Object detection모델이더라도, 중첩되는 정도가 심할경우, 기존 프레임워크로는 제대로 성능을 발휘하지 못하는데요,crowded object detection을 위한 새로운 접근법을 제시했고요 이에 특화된 loss와 NMS 알고리즘을 도입하고 결과적으로 CrowdHuman Dataset에서 90.7의 AP로 SOTA를 달성하였습니다. 논문의 리뷰를 위해 이미지처리팀 홍은기님이 자세한 리뷰 도와주셨습니다. 오늘도 많은 관심 미리 감사드립니다 ! https://youtu.be/LPC4m66YZfg

1. Detection in Crowded Scenes:
One Proposal, Multiple Predictions
Xuangeng Chu, Anlin Zheng, Xiangyu Zhang, Jian Sun
Peking University, MEGVII Technology
CVPR 2020
2021.11.21
딥러닝논문읽기모임 이미지처리팀
홍은기, 김병현, 김선옥, 안종식, 이찬혁

2. 2
목차
1. Introduction
2. Proposed Approach: Multiple Instance Prediction
3. Experiment
4. Conclusion & Discussion

3. 3
Chu et al., 2020, Detection in Crowded Scenes: One Proposal, Multiple Predictions
Introduction – Crowded Object Detection

4. 4
1. Proposed a novel approach: Multiple Instance Prediction
2. Proposed a novel loss: EMD loss
3. Proposed a novel NMS: Set NMS
4. Achieved SOTA on CrowdHuman Dataset
Contribution

5. 5
- Shao et al., 2018, CrowdHuman: A Benchmark or Detection Human in a Crowd
- https://www.crowdhuman.org/
• train/val/test: 15,000 / 4,370 / 5,000
• 470K human instances
CrowdHuman Dataset

6. 6
Chu et al., 2020, Detection in Crowded Scenes: One Proposal, Multiple Predictions
• State-of-the-art models on COCO or VOC perform poorly on CrowdHuman dataset
1) Highly overlapped instances are likely to have very similar features
2) Heavily overlapped instances are likely to be mistakenly suppressed by NMS
Fundamental difficulties in crowded object detection

7. 7
https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c
NMS (Non-Maximum Suppression)

8. 8
• For each proposal box, rather than predicting a single instance, propose a set of instances
Solution – multiple instance prediction
(a) Each proposal box predicts a single instance
(intrinsically difficult!). After NMS, only one
prediction survives.
(b) Set NMS removes duplicates from different
proposals while keeping duplicates in a proposal.
single prediction
paradigm
multiple instance
prediction

9. 9
• Step 1: assign a proposal box to ground-truths
Solution – multiple instance prediction
proposal b1
g1
g2
g3

10. 10
• Step 2: make K predictions from one proposal box
Solution – multiple instance prediction
proposal b1
g1
g2
g3
p1
p2
p3
K = 3

11. 11
• Step 3: assign predictions to ground-truths using Earth Mover’s Distance (EMD)
EMD Loss
p1
P2
P3
g1
g2
g3
background
EMD loss:
g1
g2
g3
p1
p2
p3
K = 3

12. 12
• Step 4: apply Set NMS
Set NMS
Set NMS

13. 13
Set NMS

14. 14
Architecture

15. 15
Q & A

16. 16
Experiments
• Evaluation Metrics
1) Averaged Precision (AP)
2) MR-2 Miss Rate on False Positive Per Image (FPPI) in [10-2, 100])
3) Jaccard Index
• Datasets
1) CrowdHuman
2) CityPersons
3) COCO
• Network Architecture
1) Backbone: ResNet-50 pre-trained on ImageNet
2) Head: FPN with RoIAlign
3) K = 2

17. 17
Main results and ablation study
Performance on CrowdHuman Dataset

18. 18
Comparison with various NMS strategies
Performance on CrowdHuman Dataset

19. 19
Ablation on Number of Heads
Performance on CrowdHuman Dataset

20. 20
Experiments on COCO

21. 21
Conclusion & Discussion
1. Proposed approach is not only effective on crowded scenes, but also generalizes well on
normal data.
2. Proposed approach is compatible with other one-stage & two-stage architectures.
3. A local version of DETR (Carion et al., 2020)?

22. 22
Thank you