25 citations · 34 across the 7 of their papers we have counts for
7 papers
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition
Shengqin Wang, Yongji Zhang, Hong Qi +2
Skeleton-based action recognition relies on the extraction of spatial-temporal topological information. Hypergraphs can establish prior unnatural dependencies for the skeleton. How…
Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition
Xueru Wen, Changjiang Zhou, Haotian Tang +3
Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labelin…
End-to-End Entity Detection with Proposer and Regressor
Xueru Wen, Changjiang Zhou, Haotian Tang +3
Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of…
Skeleton-based Action Recognition via Temporal-Channel Aggregation
Shengqin Wang, Yongji Zhang, Minghao Zhao +4
Skeleton-based action recognition methods are limited by the semantic extraction of spatio-temporal skeletal maps. However, current methods have difficulty in effectively combining…
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer
Zhiyuan Wu, Yu Jiang, Minghao Zhao +4
Recent applications pose requirements of both cross-domain knowledge transfer and model compression to machine learning models due to insufficient training data and limited computa…
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data
Zhiyuan Wu, Yu Jiang, Chupeng Cui +3
Semantic segmentation of road scenes is one of the key technologies for realizing autonomous driving scene perception, and the effectiveness of deep Convolutional Neural Networks(C…
Activation Map Adaptation for Effective Knowledge Distillation
Zhiyuan Wu, Hong Qi, Yu Jiang +4
Model compression becomes a recent trend due to the requirement of deploying neural networks on embedded and mobile devices. Hence, both accuracy and efficiency are of critical imp…