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20192023
most citedLearning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

62 citations · 163 across the 29 of their papers we have counts for

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Showing 2020 · cs.CVShow all

6 papers · 2 filters

cs.CV2020★ 2 cited

One for More: Selecting Generalizable Samples for Generalizable ReID Model

Enwei Zhang, Xinyang Jiang, Hao Cheng +7

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the p…

cs.CV2020

Exploring Dynamic Context for Multi-path Trajectory Prediction

Hao Cheng, Wentong Liao, Xuejiao Tang +3

To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. Howeve…

cs.CV2020

Pruning Filter in Filter

Fanxu Meng, Hao Cheng, Ke Li +4

Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pr…

cs.CV2020★ 8 cited

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians

Shizhen Zhao, Changxin Gao, Jun Zhang +7

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bou…

cs.CV2020

AMENet: Attentive Maps Encoder Network for Trajectory Prediction

Hao Cheng, Wentong Liao, Michael Ying Yang +2

Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic. How an agent move…

cs.CV2020

MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic

Hao Cheng, Wentong Liao, Michael Ying Yang +2

Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is critical for many intelligent transportation systems, such as intent detection for autonomous driving.…