6 accepted papers
Large language model (LLM)-based visual dialogue (VD) systems have made response generation for image-grounded conversations more correct and coherent. However, user engagement - the extent to which a user is interested, emotionally involved, and willing to continue the conversation - remains a chal
Affective computing has achieved notable success in recognizing explicit emotions from short, isolated dialogue segments. However, human emotions are often implicitly expressed, internally regulated, and dynamically evolve over extended interactions. Existing models struggle to disentangle internal
Existing multimodal representation learning approaches often rely on simple feature concatenation or unified transformations, which fail to effectively disentangle and leverage common and private information across different modalities in a progressive manner. Moreover, they typically lack adaptive
Scientific images often require accurate numerical representations and correct object attributes. However, current faithfulness metrics are primarily tailored toward photorealistic, real-life imagery, rendering them ill-suited for scientific image evaluation. To address this gap, we introduce a nove
Despite their remarkable performance on various tasks, Large Language Models (LLMs) still struggle with logical reasoning, particularly in complex and multi-step reasoning processes. Among various efforts to enhance LLMs' reasoning capabilities, synthesizing large-scale, high-quality logical reason…
In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this…