1. Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
CVPR 2019 Oral
Samvit Jain; Xin Wang; Joseph E. Gonzalez
University of California, Berkeley

2. Semantic Segmentation on Video Problem Definition
Input: a video clip, not any ground truth
Output: segmentation of each frame

3. Semantic Segmentation on Video Classical Approach
Segment on each single frame

4. Accel, an efficient approach
“Cheap”
feature extraction net
→ Resnet 18
“Expensive”
feature extraction net
→ Resnet 101

5. Network Architecture
Optical Flow
Warp Operation

6. Experiment Ablation Study
𝑁 𝑅 is always ResNet 101

7. Experiment Accuracy vs. inference time
On CityScapes Dataset
On CamVid Dataset

8. Experiment Comparison with Others
On CityScapes Dataset
On CamVid Dataset

9. r1. input frames
r2. Accel NR branch
r3. Accel NU branch
r4. NR+NU, Resnet18

10. Conclusions The structure is very simple.
And it looks faster and could get higher performance.
But the comparison is unfair.
The baseline is not STOA. Where is BiSeNet and ICNet??