VK-Det: Visual Knowledge Guided Prototype Learning for Open-Vocabulary Aerial Object Detection

The introduction of VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark, aims to assess the capabilities of vision-language models (VLMs) in interpreting and reasoning over visual and textual information in Vietnamese. This benchmark includes 2.5k multimodal questions across seven diverse tasks, emphasizing genuine multimodal integration rather than text-only cues.

A new approach called SubTTA has been proposed to enhance test-time adaptation (TTA) for Vision-Language Models (VLMs), addressing vulnerabilities to distribution shifts that can misguide adaptation through unreliable zero-shot predictions. SubTTA aligns the semantic subspaces of visual and textual modalities to improve the accuracy of predictions during adaptation.

A new framework named R^4 has been proposed to enhance medical image analysis by integrating Vision-Language Models (VLMs) into a multi-agent system that includes a Router, Retriever, Reflector, and Repairer, specifically focusing on chest X-ray analysis. This approach aims to improve reasoning, safety, and spatial grounding in medical imaging workflows.

A new study has introduced a subject decoupling framework for zero-shot distracted driver detection using Vision Language Models (VLMs). This approach aims to improve the accuracy of detecting driver distractions by separating appearance factors from behavioral cues, addressing a significant limitation in existing VLM-based systems.