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.
Building an open-vocabulary video clip model with better architectures, optimization and data,
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DualOpt decouples optimization by using real-time layer-wise weight decay for scratch training and weight rollback for fine-tuning to improve convergence, generalization, and reduce knowledge forgetting.
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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.
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Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning
DualOpt decouples optimization by using real-time layer-wise weight decay for scratch training and weight rollback for fine-tuning to improve convergence, generalization, and reduce knowledge forgetting.