arXiv Computer Vision
Project and Mix: Task-Semantic Prototypes for Few-Shot Image Classification
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TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment
arXiv:2606. 07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space.
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Controlling Embedding Spaces with Text-Conditioned Transformations
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Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval
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Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data
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Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs
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ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models
ProCAP introduces a probabilistic cross-attentive prompt learning framework for vision-language models like CLIP, enabling improved cross-modal interaction without updating the backbone. It jointly learns visual and textual prompt tokens, linking them via stacked bidirectional multi-head cross-attention to refine each branch across prompt depth. The method incorporates Gaussian parameterization of prompt tokens, lightweight KL and L2 regularization, and a compact symmetric InfoNCE head to align image features with class-level text representations, achieving strong few-shot base-to-novel performance and competitive transfer results across multiple datasets and benchmarks.