Titlebook: Neural Computing for Advanced Applications; 5th International Co Haijun Zhang,Xianxian Li,Qian He Conference proceedings 2025 The Editor(s)

GOAD

CE-DSLAM: A Dynamic SLAM Framework Based on?Human Contact Experience for?Escort Robotsonments. When dynamic objects appear in indoor environments, the matching of dynamic points on the object can interfere with the camera pose and leave stains on the map. Although semantic segmentation methods can remove potential dynamic objects, dynamic objects will not be detected for some movable

智力高

有組織

巨頭

Psychological Consultation Dialogue Generation Based on Multi-label Classification Model and GPThe use and effectiveness of counseling chatbots, such as how to make autoregressive models such as GPT able to understand complex counseling texts, how to be more attuned to the subject matter of the visitor‘s question to avoid meaningless answers, and how to generate gentle conversational language

Confound

DRLN: Disentangled Representation Learning Network for?Multimodal Sentiment Analysis Previously, a considerable amount of research had commonly focused on fusing these three modalities equally, neglecting the importance of text modality and the influence of modal heterogeneity on fusion. To address this issue, we propose the Disentangled Representation Learning Network (DRLN) which

BOLT

馬賽克

Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limi

flaunt

Rehabilitation Training Program Recommendation System Based on ALBERT-LDA Modelents with physical disability is a hot spot of current research. Traditional rehabilitation training program recommendation methods often ignore the contextual semantic information and potential semantic information in physical evaluation, resulting in inaccurate semantic expression and low accuracy

AMITY

殘酷的地方

Explicit Facial Attribute Disentanglement for?Hierarchical Relationships Detectionributes unchanged. However, editing multiple facial attributes often results in unintentional alterations to other unedited attributes. This primarily occurs because current generative models do not consider the hierarchical relationships among facial attributes, treating each attribute independentl