6 accepted papers
Explainable Recommendation (ER) aims to enhance recommendation transparency and prediction accuracy by providing faithful and persuasive explanations. However, Multi-Modal Multi-Interest Explainable Recommendation (MMER) is particularly challenging in two aspects: effectively utilizing diverse multi
Multi-modal fusion perception enhances robotic performance in complex tasks by providing more comprehensive information than single modality. While tactile and proprioceptive sensing are effective for direct contact tasks like grasping, current research mainly focuses on vision-language fusion, negl
Multi-modal perception plays a crucial role in preventing deformation and damage during the robotic manipulation of deformable objects. However, integrating new heterogeneous modalities into existing robotic perception frameworks remains a significant challenge, primarily due to the need for massive
Embodied visual navigation has witnessed significant advancements. However, most studies commonly assume that environments are static and contain at least one collision-free path. In human environments, agents frequently encounter challenges when navigating through scenes with disarranged objects. I
Maps are indispensable foundation for autonomous driving to provide prior knowledge of road structures. Constructing maps in a crowdsourcing manner has been a research hotspot in order to achieve lower map production cost and higher update frequency. Existing pipelines of crowdsourcing mapping const
Learning to achieve a user-specified objective from a random position in unseen environments is challenging for image-guided navigation agents. The abilities of long-horizon reasoning and semantic understanding are still lacking. Inspired by the human memory mechanism, we introduce a neural multi-st