This work proposes Progressively Disentangled and Recurrent Prompt Tuning (PDRPT), an edge-efficient framework that decouples object and state updates before joint refinement, suppresses traction force from highly-entangled prompts, and preserves alignment with the natural language space of CLIP.
Vision-language models (VLMs), such as contrastive language-image pre-training (CLIP), exhibit powerful zero-shot generalization capabilities. Parameter-efficient fine-tuning (PEFT) techniques, notably prompt learning, have been extensively explored to adapt these models to downstream tasks. However, their efficacy rem...
Tengfei Gong, Jun-Lin Wu, Yaxioong Chen et al.· IEEE Transactions on Geoscie...· 0 citations
This work argues that cross-modal alignment is implicitly captured in the information-compression trajectory, and proposes LLaVAFlow, an information-theoretic distillation framework that preserves alignment flow and enhances both downstream performance and generalization.
Mu-Yao Yuan, Mu-Yan Jiao, Jiang-Yong Ying et al.· 0 citations
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
Yiwei Sun, Chuan-Bin Liu, Shan-Cheng Fang et al.· International Journal of Com...· 0 citations
GramLoop is introduced, a training-free framework that replays a short transformer window and controls each replay through final-layer cosine-Gram consistency, which improves object detection and semantic segmentation under corruptions, perturbations, and natural shifts.
Yang Chen, Can-Yu Shen, Xin-Zhe Rao et al.· 0 citations
MLDA is introduced, a test-time adaptation framework for CZSL that jointly derives multi-level prototypes and performs dynamic vision–language alignment and leverages optimal transport to achieve consistent matching of semantically salient information across modalities.
Miao-Ge Li, Yu Liu, Jingcai Guo· Proceedings of the Thirty-Fi...· 0 citations
This work proposes VCF-CLIP, a visual context-driven fine-grained prompt learning framework built upon CLIP, and proposes the prompt prototype learning (PPL) strategy, which learns a pair of unified prompt prototypes representing general normal and anomalous states in a loss-guided manner, thereby eliminating the need...
Kaiwen Fu, Fei Qi, Chengyuan Chang et al.· IEEE Transactions on Neural...· 0 citations
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