GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
Atprompt: Textual prompt learning with embedded attributes
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FedMPT applies causal modeling and LLM-driven condition prompts with optimal transport and gating to perform federated multi-label prompt tuning of VLMs, claiming competitive results on benchmarks.
citing papers explorer
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GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models
GA2-CLIP uses generic attribute anchors and coupled hard-soft prompts to preserve generalization in prompt-tuned video-language models on base-to-new class tasks.
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FedMPT: Federated Multi-label Prompt Tuning of Vision-Language Models
FedMPT applies causal modeling and LLM-driven condition prompts with optimal transport and gating to perform federated multi-label prompt tuning of VLMs, claiming competitive results on benchmarks.