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Huimin Ma

15 papers hereh-index 206.9k citations102 works total

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

author position
  • middle author10
  • last author5

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CV12
  • cs.AI2
  • cs.LG1
same name
  • Huimin Ma — 16 papers, h 10
  • Huimin Ma — 13 papers, h 15
  • Huimin Ma — 6 papers, h 8
  • Huimin Ma — 1 paper
  • Huimin Ma — 1 paper, h 3
  • Huimin Ma — 1 paper, h 2

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
20162021
most citedEdge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

127 citations · 409 across the 15 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 5 cited

Semantic Head Enhanced Pedestrian Detection in a Crowd

Ruiqi Lu, Huimin Ma

Pedestrian detection in the crowd is a challenging task because of intra-class occlusion. More prior information is needed for the detector to be robust against it. Human head area…

cs.CV2019★ 2 cited

WSOD with PSNet and Box Regression

Sheng Yi, Xi Li, Huimin Ma

Weakly supervised object detection(WSOD) task uses only image-level annotations to train object detection task. WSOD does not require time-consuming instance-level annotations, so…

cs.CV2019

Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor

Ruiqi Lu, Huimin Ma

Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection. Traditionally adopted Intersection over Union (IoU) measurement does not…

cs.LG2019★ 3 cited

Pretrain Soft Q-Learning with Imperfect Demonstrations

Xiaoqin Zhang, Yunfei Li, Huimin Ma +1

Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent…

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