25 citations · 70 across the 26 of their papers we have counts for
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cs.CV2022★ 11 cited
Latency-aware Spatial-wise Dynamic Networks
Yizeng Han, Zhihang Yuan, Yifan Pu +4
Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixe…
cs.RO2022★ 1 cited
Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds
Kangchen Lv, Mingrui Yu, Yifan Pu +3
Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remai…
cs.CV2022★ 2 cited
Learning to Weight Samples for Dynamic Early-exiting Networks
Yizeng Han, Yifan Pu, Zihang Lai +6
Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks…