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cs.CV2025
Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval
Yifan Wang, Tao Wang, Chenwei Tang +5
Recently, prompt learning has demonstrated remarkable success in adapting pre-trained Vision-Language Models (VLMs) to various downstream tasks such as image classification. Howeve…
cs.CV2025
Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment
Yang Liu, Mengyuan Liu, Shudong Huang +1
Learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data,…
cs.CV2025
Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Yang Liu, Wentao Feng, Zhuoyao Liu +2
Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to fin…