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ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents On the Robustness of Watermarking for Autoregressive Image Generation Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference Anthropogenic Regional Adaptation in Multimodal Vision-Language Model From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems BoxTuning: Directly Injecting the Object Box for Multimodal Model Fine-Tuning Semantic-Geometric Dual Compression: Training-Free Visual Token Reduction for Ultra-High-Resolution Remote Sensing Understanding Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance Retinal Cyst Detection from Optical Coherence Tomography Images LoViF 2026 The First Challenge on Weather Removal in Videos STORM: End-to-End Referring Multi-Object Tracking in Videos Data-Efficient Surgical Phase Segmentation in Small-Incision Cataract Surgery: A Controlled Study of Vision Foundation Models Rethinking the Diffusion Model from a Langevin Perspective Zero-shot World Models Are Developmentally Efficient Learners Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation Degradation-Consistent Paired Training for Robust AI-Generated Image Detection FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer Demographic and Linguistic Bias Evaluation in Omnimodal Language Models FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation Cross-Cultural Value Awareness in Large Vision-Language Models I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception Genie 4D: Semantic-Prior-Guided 4D Dynamic Scene Reconstruction Efficient Personalization of Generative User Interfaces Is There Knowledge Left to Extract? Evidence of Fragility in Medically Fine-Tuned Vision-Language Models ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering Rays as Pixels: Learning A Joint Distribution of Videos and Camera Trajectories PhysInOne: Visual Physics Learning and Reasoning in One Suite Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation Neural Distribution Prior for LiDAR Out-of-Distribution Detection Adding Another Dimension to Image-based Animal Detection Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval Detecting Diffusion-generated Images via Dynamic Assembly Forests Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation Tora3: 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for Text-based Person Search Assessing Privacy Preservation and Utility in Online Vision-Language Models R3PM-Net: Real-time, Robust, Real-world Point Matching Network Tipiano: Cascaded Piano Hand Motion Synthesis via Fingertip Priors Belief-Aware VLM Model for Human-like Reasoning GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents B-MoE: A Body-Part-Aware Mixture-of-Experts "All Parts Matter" Approach to Micro-Action Recognition FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention BiCLIP: Domain Canonicalization via Structured Geometric Transformation Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations MerNav: A Highly Generalizable Memory-Execute-Review Framework for Zero-Shot Object Goal Navigation Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers When & How to Write for Personalized Demand-aware Query Rewriting in Video Search Relational Visual Similarity Enhancing Geo-localization for Crowdsourced Flood Imagery via LLM-Guided Attention GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models OmniPrism: Learning Disentangled Visual Concept for Image Generation MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions
Lesion-Aware Visual-Language Fusion for Automated Image Captioning of Ulcerative Colitis Endoscopic Examinations
Alexis Ivan Lopez Escamilla, Gilberto Ochoa, Sharib Al · 2025-09-03 · via cs.CV updates on arXiv.org

We present a lesion-aware image captioning framework for ulcerative colitis (UC). The model integrates ResNet embeddings, Grad-CAM heatmaps, and CBAM-enhanced attention with a T5 decoder. Clinical metadata (MES score 0-3, vascular pattern, bleeding, erythema, friability, ulceration) is injected as natural-language prompts to guide caption generation. The system produces structured, interpretable descriptions aligned with clinical practice and provides MES classification and lesion tags. Compared with baselines, our approach improves caption quality and MES classification accuracy, supporting reliable endoscopic reporting.

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