Skin-R1: Toward Trustworthy Clinical Reasoning for Dermatological Diagnosis

The SA-FARI dataset is the largest open-source multi-animal tracking (MAT) dataset for wildlife conservation, comprising 11,609 camera trap videos collected over ten years from 741 locations across four continents. It includes 99 species categories and features extensive annotations, totaling approximately 46 hours of footage with 16,224 masklet identities and 942,702 bounding boxes. This dataset aims to improve automated video analysis for applications like individual re-identification and behavior recognition.

The article discusses EyeVLA, a robotic eyeball designed for active visual perception in embodied AI systems. Unlike traditional models that passively process images, EyeVLA actively acquires detailed information while managing spatial constraints. This innovation aims to enhance the effectiveness of robotic applications in open-world environments by integrating action tokens with vision-language models (VLMs) for improved understanding and interaction.

The paper introduces VLM3D, a novel framework that utilizes vision-language models (VLMs) to enhance text-to-3D generation. It addresses two major limitations in current models: the lack of fine-grained semantic alignment and inadequate 3D spatial understanding. VLM3D employs a dual-query critic signal to evaluate both semantic fidelity and geometric coherence, significantly improving the generation process. The framework demonstrates its effectiveness across different paradigms, marking a step forward in 3D generation technology.

The article presents a new framework called GMAT, which enhances Multiple Instance Learning (MIL) for whole slide image (WSI) classification. By integrating vision-language models (VLMs), GMAT aims to improve the generation of clinical descriptions that are more expressive and medically specific. This addresses limitations in existing methods that rely on large language models (LLMs) for generating descriptions, which often lack domain grounding and detailed medical specificity, thus improving alignment with visual features.

The paper discusses the challenges of test-time prompt tuning for vision-language models, highlighting the issue of prompt optimization bias that can lead to suboptimal performance in downstream tasks. It identifies two main causes: the model's focus on entropy minimization, which may overlook prediction accuracy, and data misalignment between visual and textual modalities. To address these issues, the authors propose a new method called Doubly Debiased Test-Time Prompt Tuning, aimed at improving model performance in zero-shot settings.