Adaptive transform coding for semantic feature compression, motivated by the conditional rate-distortion function of a Gaussian mixture model, outperforms or matches neural compression methods on vision backbone features while remaining flexible and interpretable.
Learning transferable visual models from natural language supervi- sion
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EV-CLIP introduces mask and context visual prompts to adapt CLIP for improved few-shot video action recognition under visual challenges such as low light and egocentric views, outperforming other efficient methods with backbone-scale-independent efficiency.
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Adaptive Transform Coding for Semantic Compression
Adaptive transform coding for semantic feature compression, motivated by the conditional rate-distortion function of a Gaussian mixture model, outperforms or matches neural compression methods on vision backbone features while remaining flexible and interpretable.
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EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges
EV-CLIP introduces mask and context visual prompts to adapt CLIP for improved few-shot video action recognition under visual challenges such as low light and egocentric views, outperforming other efficient methods with backbone-scale-independent efficiency.