4 accepted papers
Recent Omni-MLLMs are driving a paradigm shift in multimodal emotion recognition from label-only prediction toward *Multimodal Emotion Reasoning* (MER), where models output both emotions and textual explanations grounded in visual, acoustic, and linguistic signals. However, we show that current emot…
Vision-Language Models (VLMs) are limited by static knowledge and insufficient fine-grained visual analysis, hindering their performance on knowledge-intensive and visually complex tasks. While recent research has explored VLMs that employ external tools like search or cropping to enhance model perf
Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a persistent challenge. Although recent agentic approaches incorporate tool use, they often neglect critical execution feedback. Consequently, they suffer …
Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-aware representation that quantizes the physical 3D scene into structured grid map with semantic labels per cell, termed…