| Paper ID | SS-15.5 |
| Paper Title |
LATENT SPACE MOTION ANALYSIS FOR COLLABORATIVE INTELLIGENCE |
| Authors |
Mateen Ulhaq, Ivan Bajic, Simon Fraser University, Canada |
| Session | SS-15: Signal Processing for Collaborative Intelligence |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Signal Processing for Collaborative Intelligence |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
When the input to a deep neural network (DNN) is a video signal, a sequence of feature tensors is produced at the intermediate layers of the model. If neighboring frames of the input video are related through motion, a natural question is, ``what is the relationship between the corresponding feature tensors?'' By analyzing the effect of common DNN operations on optical flow, we show that the motion present in each channel of a feature tensor is approximately equal to the scaled version of the input motion. The analysis is validated through experiments utilizing common motion models. |
