OpenGVLab / OpenGVLab/InternVideo
Attention-Guided Token Selection Algorithm in InternVideo 2.5
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- Dominant language
- Python
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Description
Hi OpenGVLab team, thank you very much for all your excellent models.
In the InternVideo 2.5 paper section 3.1, it is mentioned that:
(1) uniform token pruning in early layers to maintain structural integrity while reducing computational overhead, and (2) attention-guided token selection in deeper layers to retain task-relevant essences.
Regarding the second point, attention-guided token selection, could you please share the specific method you used? Since this process involves attention weight, it may not be compatible with Flash Attention 2. Does this lead to excessive memory consumption?
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Research direction
The issue identifies InternVideo 2.5 paper section 3.1 as the starting point; review that section and locate the corresponding implementation in the repository. Done would mean documenting the attention-guided token-selection method and addressing its compatibility with Flash Attention 2 and its memory implications.
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Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100