CNN backbones like ResNet50 or Xception extract frame-level forensic embeddings.
Recurrent layers (like GRU or LSTM ) capture motion inconsistencies or action sequences over time. 2022-12-02 17-24-24.mp4
The final "deep features" or concepts are often weighted based on their frequency and relevance within the metadata. For a video like "2022-12-02 17-24-24.mp4" in the "screaming kid" study, the top extracted concepts might include terms like like "joy" or "insanity". CNN backbones like ResNet50 or Xception extract frame-level
Instead of relying solely on raw pixels, "deep" insights are generated by analyzing the relationships between different data streams. 2022-12-02 17-24-24.mp4