2. Motivation
State-of-the-art two-stage instance segmentation methods depend heavily on feature localization to produce masks.

3. Method
generating a dictionary of non-local prototype masks over the entire image
predicting a set of linear combination coefficients per instance

4. Framework

5. Protonet

6. Head Architecture

7. Other Improvements Fast NMS
we simply allow already-removed detections to suppress other detections
first compute a c × n × n pairwise IoU matrix X for the top n detections sorted descending by score
find which detections to remove by checking if there are any higher-scoring detections with a corresponding IoU greater than some threshold t.

8. Other Improvements Semantic Segmentation Loss
we simply attach a 1x1 conv layer with c output channels directly to the largest feature map (P3) in our backbone

9. Experiments

10. Experiments

11. Experiments

12. Experiments

14. Motivation
how and where to add the supervision from detection ground-truth and the one from a different network

15. feature mimic

16. Two-stage Mimic
The prediction of the detector in Faster-RCNN or R-FCN detector can be regarded as a classification task.
the category classification information learned by the large model can be passed to the small network

17. Result

18. Result

20. Motivation
Human can recognize the ”gist” of the scene and it is accomplished by relying on relevant prior knowledge.

21. contributions
a memory-guided interleaving framework where multiple feature extractors
an adaptive interleaving policy
demonstrate on-device the fastest mobile video detection model

22. Framework

23. Interleaved Models SSD-style [24] detection
f0 optimized for accuracy f1 optimized for speed
Shared memory

24. Interleaved Models Memory Module Bottlenecking
Divide the LSTM state into groups and use grouped convolutions

25. Result

26. Adaptive Interleaving Policy
We denote the state as:
The action history is a binary vector of length 20.
For all k, the k-th entry of η is 1 if f1 was run k steps ago and 0 otherwise.

27. Adaptive Interleaving Policy
we define the reward as the sum of a speed reward and an accuracy reward.
For the speed reward, we simply define a positive constant γ and give γ reward when f1 is run.

28. Adaptive Interleaving Policy
For the accuracy reward, we compute the detection losses after running each feature extractor.
take the loss difference between the minimum-loss feature extractor and the selected feature extractor

29. Adaptive Interleaving Policy

30. Inference Optimizations
Asynchronous Inference
Quantization

31. Experiments

32. Experiments