4 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
Haowei Zhu, Dehua Tang, Ji Liu +12
Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resou…
Towards Scale-Aware Full Surround Monodepth with Transformers
Yuchen Yang, Xinyi Wang, Dong Li +3
Full surround monodepth (FSM) methods can learn from multiple camera views simultaneously in a self-supervised manner to predict the scale-aware depth, which is more practical for…
Sparse Laneformer
Ji Liu, Zifeng Zhang, Mingjie Lu +7
Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, whic…
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Ji Liu, Dehua Tang, Yuanxian Huang +9
Traditional channel-wise pruning methods by reducing network channels struggle to effectively prune efficient CNN models with depth-wise convolutional layers and certain efficient…
Separated RoadTopoFormer
Mingjie Lu, Yuanxian Huang, Ji Liu +3
Understanding driving scenarios is crucial to realizing autonomous driving. Previous works such as map learning and BEV lane detection neglect the connection relationship between l…