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researcher

Ming Liang

17 papers hereh-index 164k citations19 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author14

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV15
  • cs.LG1
  • cs.RO1
same name
  • Ming Liang — 9 papers, h 7
  • Ming Liang — 4 papers, h 9
  • Ming Liang — 2 papers, h 1
  • Ming Liang — 1 paper, h 2
  • Ming Liang — 1 paper, h 3
  • Ming Liang — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192021
most citedHDNET: Exploiting HD Maps for 3D Object Detection

243 citations · 416 across the 17 of their papers we have counts for

collaborators
Showing 2021 · cs.CVShow all

4 papers · 2 filters

cs.CV2021★ 11 cited

LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting

Wenyuan Zeng, Ming Liang, Renjie Liao +1

Forecasting the future behaviors of dynamic actors is an important task in many robotics applications such as self-driving. It is extremely challenging as actors have latent intent…

cs.CV2021★ 17 cited

Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving

James Tu, Huichen Li, Xinchen Yan +6

Modern self-driving perception systems have been shown to improve upon processing complementary inputs such as LiDAR with images. In isolation, 2D images have been found to be extr…

cs.CV2021★ 4 cited

PLUMENet: Efficient 3D Object Detection from Stereo Images

Yan Wang, Bin Yang, Rui Hu +2

3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accur…

cs.CV2021★ 26 cited

Auto4D: Learning to Label 4D Objects from Sequential Point Clouds

Bin Yang, Min Bai, Ming Liang +2

In the past few years we have seen great advances in object perception (particularly in 4D space-time dimensions) thanks to deep learning methods. However, they typically rely on l…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.