4 accepted papers
Multimodal emotion recognition in conversation (ERC) has attracted more attention due to its wide application in multiple fields. Most previous related works focused on directly fusing different modalities, resulting in the excessive introduction of irrelevant multimodal information, which interfere…
Understanding how humans cooperatively rearrange household objects is critical for VR/AR and human-robot interaction. However, in-depth studies on modeling these behaviors are under-researched due to the lack of relevant datasets. We fill this gap by presenting CORE4D, a novel large-scale 4D human-o…
Multimodal Emotion Recognition in Conversation (ERC) has gained significant attention due to its wide-ranging applications in diverse areas. However, most previous approaches focused on modeling context at the semantic level, neglecting the context of dependency information at the emotional level. I…
In multi-turn dialogue generation, it’s still a challenge to generate coherent responses given dialogue history, which requires neural models to learn complex semantic structures between responses and contexts. It’s practical that a multi-turn conversation takes place under a constant background, su…