Enhancement: Attention-Weighted Pooling for Video Embeddings

[thewildofficial]

Overview

Enhance video embedding generation with attention-weighted pooling as demonstrated in the research notebooks.

Current Implementation

Currently using simple mean pooling:

mean_embedding = np.mean(frame_embeddings_array, axis=0)

Proposed Enhancement

Implement attention-weighted pooling from notebooks:

• Compute attention scores using temperature-scaled dot product
• Apply softmax to get attention weights
• Weight frame embeddings by importance
• Temperature: 0.08 (as per notebook research)

Benefits

• Better video representation (emphasizes important frames)
• Improves clustering quality
• Aligns with research findings
• More accurate semantic similarity

Implementation

Update MediaEmbedder.encode_video_keyframes() to use attention-weighted pooling instead of simple mean pooling.

References

• Notebook: unified_media_clustering.ipynb
• Notebook: video_embedding_semantic_search.ipynb
• Technical Spec: Section 5 (Media Processing Pipeline)

Priority

Medium - Quality improvement

[thewildofficial]

✅ IMPLEMENTED

Attention-weighted pooling has been implemented in src/media/embedder.py:

• Uses temperature-scaled attention (0.08) as per notebook research
• Falls back to mean pooling if attention fails
• Aligns with unified_media_clustering.ipynb and video_embedding_semantic_search.ipynb

Implementation Details:

• Computes attention scores using dot product with mean embedding
• Applies softmax with temperature scaling
• Weighted sum of frame embeddings
• Proper normalization

Status: Ready for testing