no code implementations • 12 Dec 2022 • Chenliang Gu, Changan Wang, Bin-Bin Gao, Jun Liu, Tianliang Zhang
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution.
no code implementations • 12 Dec 2022 • Tianliang Zhang, Qixiang Ye, Baochang Zhang, Jianzhuang Liu, Xiaopeng Zhang, Qi Tian
FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module.
no code implementations • 12 Dec 2022 • Tianliang Zhang, Zhenjun Han, Huijuan Xu, Baochang Zhang, Qixiang Ye
In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time.
no code implementations • 23 Nov 2022 • Jiawei Zhan, Jun Liu, Wei Tang, Guannan Jiang, Xi Wang, Bin-Bin Gao, Tianliang Zhang, Wenlong Wu, Wei zhang, Chengjie Wang, Yuan Xie
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
no code implementations • 4 Nov 2021 • WeiFu Fu, Congchong Nie, Ting Sun, Jun Liu, Tianliang Zhang, Yong liu
Our method focuses on the problem in following two aspects: the long-tail distribution and the segmentation quality of mask and boundary.
Ranked #3 on Instance Segmentation on LVIS v1.0 val
3 code implementations • CVPR 2020 • Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang
In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.
Ranked #116 on Object Detection on COCO test-dev
no code implementations • CVPR 2017 • Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved.