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.
By Hiwa Azeez Abbas, Fatemeh Daneshfar, Moloud Abdar
arXiv:2609.36680v1 Announce Type: new
Abstract: Visual reprogramming adapts pretrained models to downstream tasks by modifying their input and output interfaces while keeping the backbone fixed. In v...
By Zizhao Li, Chengyi Cai, Mohammed Yaqoob Ansari, Feng Liu, Joseph West, Kourosh Khoshelham
arXiv:2602. 21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights.
By Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani
Prompt learning modifies vision‑language models by optimizing continuous prompt vectors, yet the resulting prompts are hard to interpret in natural language. PromptSpLiCE is a post‑hoc method that rewrites each class‑conditioned text embedding as a sparse mix of concepts from a fixed dictionary, enabling a direct comparison of concept profiles before and after prompt learning. Across 11 image‑classification datasets, the method shows that only about 1.6 of the initial top‑10 concepts remain after learning, and that larger profile changes correlate with higher accuracy gains, while a derived gradient expression offers geometric insight into loss sensitivity.
By Ryo Kamiya, Hiroshi Kera, Kazuhiko Kawamoto
The paper introduces EvoPrompt, a framework for adapting vision‑language models to new tasks with limited data while preventing catastrophic forgetting. EvoPrompt uses a Modality‑Shared Prompt Projector to create hierarchical prompts and an evolutionary training strategy that separates low‑rank updates into directional and magnitude components, preserving learned semantic directions. Experiments show that EvoPrompt achieves state‑of‑the‑art few‑shot performance while maintaining the original zero‑shot capabilities of the pre‑trained models.
By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li
arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.
By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia
Hidden‑Shot introduces an implicit prompt mechanism that extracts task‑specific visual information and merges it with in‑task processing to boost one‑shot performance on new low‑level vision tasks. The method injects this prompt cost‑effectively while minimally altering the base generalist model’s architecture. A data‑driven evaluation framework, C/U assessment, is proposed to systematically test generalization across conventional and unconventional tasks, and experiments on seven and ten datasets show Hidden‑Shot outperforming state‑of‑the‑art models.
By Shao-Jun Xia, Xianzheng Ma, Zichong Meng
arXiv:2607. 28967v1 Announce Type: cross Abstract: Prompt tuning adapts vision--language models with few trainable parameters, but existing approaches trade off efficiency and adaptation: static textual prompts can overfit source classes, image-conditioned prompts add per-instance computation, and multimodal tuning modifies the visual branch.
By Pouya Parsa, Raoof Zare Moayedi, Seongjin Choi
ReViCo (Real Visual Correction) is a new benchmark that tests Vision Language Models (VLMs) on the task of correcting text errors in real‑world images, requiring deep understanding of visual text and its context. The study evaluates VLMs using both prompt‑based and targeted training approaches, revealing a significant performance gap between current models and humans. The results show that most VLMs struggle to accurately perceive visual text, leading to frequent correction mistakes, thereby underscoring the need for more robust, text‑aware VLMs.
By Bojun Zhang, Junhong Liang, Feifei Zhai, Fengxian Ji, Yu Zhou
arXiv:2607. 14125v1 Announce Type: new Abstract: Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.
By Ruijiang Dong, Zesheng Ye, Jianzhong Qi, Lei Feng, Feng Liu, Gang Niu, Masashi Sugiyama
Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown remarkable performance in text-vision tasks, their robustness and transferability to different document domains remains underexplored.
arXiv:2509.23928v3 Announce Type: replace-cross
Abstract: Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Mo...
By Zhinan Xie, Peisong Wang, Shuang Qiu, Jian Cheng