1. CVPR19

2. Motivation From image to video Collection of images？
1.5fps
Fail to realize the potential offered by the preceding frames
Feature reuse and warping
Constrained by video dynamics

3. Framework (Accel)
Reference branch
Update branch
Correction
Anchoring

4. Network design Feature subnetwork Nfeat Task subnetwork Ntask
Remove conv5 (stride 32 to 16)
Task subnetwork Ntask
Feature projection: Conv 1*1
Scoring label: Conv 1*1
Up-sampling Block: x16
Output block
Softmax and argmax

5. Accel
Reference NRfeat
Resnet 101
Update NUfeat
Resnet-18 ~ resnet-101

6. Algorithm
If is_keyframe:
Execute
Save
Else:
W: FlowNet SF: Conv1*1

7. Training Pretraining reference network and update network
Fine-tuning reference network and update network
Training Accel
keyframe interval n
Ij-(n-1) as keyframe
CE loss

8. Experiments

9. Experiments

10. Experiments

11. CVPR19

12. Motivation
“However, we find that segmentation performance across the entire video varies dramatically when selecting an alternative frame for annotation. ”

13. Motivation How to select the best frame for annotation?
Given m videos (n frames for each video)
Whole video
Input: video output: frame index
LSTM or 3D conv
m training samples
Performance of images
m*n training samples
Relative performance of images
m* 𝑛 2
m* 𝑛 𝑘 with reference frames

14. BubbleNet
Loss function:
Frame indices:
Generating Performance Labels

15. BubbleNet How many passes? Reference frames Bubble sort: 1
BubbleNet: 1 (n forward passes)

16. Experiments

17. Experiments

18. Experiments