17 citations · 69 across the 18 of their papers we have counts for
18 papers
MLP Can Be A Good Transformer Learner
Sihao Lin, Pumeng Lyu, Dongrui Liu +4
Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. Previous token pruning works motivate their methods from the view of comput…
Self-Supervised Multi-Frame Neural Scene Flow
Dongrui Liu, Daqi Liu, Xueqian Li +5
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their su…
DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions
Guangrun Wang, Changlin Li, Liuchun Yuan +5
Neural Architecture Search (NAS), aiming at automatically designing neural architectures by machines, has been considered a key step toward automatic machine learning. One notable…
MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deployment
Hongtao Huang, Xiaojun Chang, Wen Hu +1
Recent years have seen the explosion of edge intelligence with powerful Deep Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud servers and subsequently…
No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling
Xuwei Xu, Changlin Li, Yudong Chen +3
Vision Transformers (ViTs) have demonstrated outstanding performance in computer vision tasks, yet their high computational complexity prevents their deployment in computing resour…
Mask Propagation for Efficient Video Semantic Segmentation
Yuetian Weng, Mingfei Han, Haoyu He +4
Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending im…