Comments (2)
After discussing some details, I think the DataLoader should provide:
· (W, N, 256) # audio embeddings
· (W, N, 256) # face embeddings
· (W, N, 68*3) # facial landmarks
· (W, N, 33*5) # body landmarks
· (W, N, 2*21*5) # hand landmarks
from multimodal-depression-from-video.
Done.
from multimodal-depression-from-video.
Related Issues (20)
- Implement MajorityVoting Evaluator
- Implement Temporal Evaluator
- Extract Eye Gaze for each video HOT 2
- Extract blinking patterns for each video HOT 2
- Implement Encoders for gaze / blinking HOT 2
- Implement Perceiver Architecture HOT 1
- Common Positional Encodings for all modalities
- Fix normalization for landmarks HOT 1
- Experiments before final experiments
- Check if evaluate.py is working.
- Come up with some nice statistics for blinking patterns
- Come up with a nice visualization for the gaze.
- Add mean predictions to TemporalEvaluator HOT 1
- Uncomment loss weights to configs/base_config.yaml
- Try 0.01 lr in CyclicLR HOT 1
- Thanks for your great work! Can share the implement of the Attribution scores as shown in Fig 5?
- while setting variables HOT 27
- correct structure to store the videos HOT 1
- Why does the value of NAN appear when dealing with emonet embedding? HOT 1
- The problems of size mismatch are occurring
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