35 citations · 80 across the 13 of their papers we have counts for
5 papers · 1 filter
Feature Calibration Network for Occluded Pedestrian Detection
Tianliang Zhang, Qixiang Ye, Baochang Zhang +3
Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the…
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection
Tianliang Zhang, Zhenjun Han, Huijuan Xu +2
Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Exi…
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation
WeiFu Fu, CongChong Nie, Ting Sun +3
This report introduces the technical details of the team FuXi-Fresher for LVIS Challenge 2021. Our method focuses on the problem in following two aspects: the long-tail distributio…
Multiple Anchor Learning for Visual Object Detection
Wei Ke, Tianliang Zhang, Zeyi Huang +3
Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate…
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
Qixiang Ye, Tianliang Zhang, Qiang Qiu +3
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach i…