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arXiv 2602.07017cs.CVcs.AI
XAI-CLIP: 通过区域感兴趣引导扰动框架实现多模态视觉-语言模型中可解释的医学图像分割
XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models
King Fahd University of Petroleum and Minerals（国王法赫德石油和矿物大学）；Massachusetts Institute of Technology（麻省理工学院）；Imperial College London（伦敦帝国学院）；KFUPM-SDAIA Joint Research Centre for Artificial Intelligence（KFUPM-SDAIA联合人工智能研究中心）
Thuraya Alzubaidi, Sana Ammar, Maryam Alsharqi, Islem Rekik, Muzammil Behzad
Medical image segmentation is a critical component of clinical workflows, enabling accurate diagnosis, treatment planning, and disease monitoring. However, despite the superior performance of transformer-based models over convolutional architectures, their limited interpretability remains a major obstacle to clinical trust and deployment. Existing explainable artificial intelligence (XAI) techniques, including gradient-based saliency methods and perturbation-based approaches, are often computationally expensive, require numerous forward passes, and frequently produce noisy or anatomically irrelevant explanations. To address these limitations, we propose XAI-CLIP, an ROI-guided perturbation framework that leverages multimodal vision-language model embeddings to localize clinically meaningful anatomical regions and guide the explanation process. By integrating language-informed region localization with medical image segmentation and applying targeted, region-aware perturbations, the proposed method generates clearer, boundary-aware saliency maps while substantially reducing computational overhead. Experiments conducted on the FLARE22 and CHAOS datasets demonstrate that XAI-CLIP achieves up to a 60\% reduction in runtime, a 44.6\% improvement in dice score, and a 96.7\% increase in Intersection-over-Union for occlusion-based explanations compared to conventional perturbation methods. Qualitative results further confirm cleaner and more anatomically consistent attribution maps with fewer artifacts, highlighting that the incorporation of multimodal vision-language representations into perturbation-based XAI frameworks significantly enhances both interpretability and efficiency, thereby enabling transparent and clinically deployable medical image segmentation systems.
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
School of Computer Science and Engineering（计算机科学与工程学院）；School of Mechanical and Electrical Engineering（机械与电气工程学院）；University of Electronic Science and Technology of China（电子科技大学）；School of Medicine, University of Electronic Science and Technology of China（电子科技大学医学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
School of Electronic Engineering & Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；National Technical University Athens（雅典国立技术大学）
Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability
U2IS, ENSTA, Institut Polytechnique de Paris（巴黎综合理工学院ENSTA学院U2IS实验室）；ISIR, Université Sorbonne, Pierre et Marie Curie（索邦大学皮埃尔和玛丽·居里分校ISIR实验室）；AMIAD, Pôle Recherche（AMIAD研究部）
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
Northwestern University, Evanston, IL, USA（西北大学）；University of California, Berkeley, CA, USA（加州大学伯克利分校）；Eastern Switzerland University of Applied Sciences, Rapperswil, SG, CH（东瑞士应用科学大学）
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
ML-Labs, Dublin City University（都柏林城市大学 ML-Labs）；ML-Labs, University College Dublin（都柏林大学 ML-Labs）；Centre for Applied AI (CeADAR), University College Dublin（都柏林大学应用人工智能中心 (CeADAR)）；Insight SFI Centre for Data Analytics, Dublin City University（都柏林城市大学 Insight SFI 数据分析中心）
Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in both base and novel classes. Additionally, our novel RoI-based masked attention and RoI pruning techniques help leverage the zero-shot classification ability of the Vision Transformer-based CLIP, resulting in improved detection performance at minimal computational cost. Our experiments on the OV-COCO and OVLVIS datasets demonstrate that Prompt-OVD achieves an impressive 21.2 times faster inference speed…
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
Unité d’Informatique et d’Ingénierie des Systèmes ENSTA Paris, Institut Polytechnique de Paris（ENSTA巴黎信息与系统工程单位，巴黎理工学院）；Department of Information Engineering and Computer Science University of Trento（信息工程与计算机科学系，特伦托大学）
Semantic-Spatial Feature Fusion with Dynamic Graph Refinement for Remote Sensing Image Captioning
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）
State University of New York at Buffalo（纽约州立大学布法罗分校）；Amazon（亚马逊）
Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attenti…
SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary Detection
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
Why Does It Look There? Structured Explanations for Image Classification
Department of Computer Science, Tulane University, New Orleans LA 70118, USA（计算机科学系，路易斯安那大学，新奥尔良 LA 70118, 美国）；Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans LA 70112, USA（生物化学与分子生物学系，路易斯安那大学医学院，新奥尔良 LA 70112, 美国）
The Hebrew University of Jerusalem（希伯来大学）；Reichman University（赖希曼大学）
Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions o…
