Search Results for author: Tianliang Zhang

Found 7 papers, 1 papers with code

HDNet: A Hierarchically Decoupled Network for Crowd Counting

no code implementations12 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.

Crowd Counting Density Estimation +1

Feature Calibration Network for Occluded Pedestrian Detection

no code implementations12 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.

Pedestrian Detection

CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection

no code implementations12 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.

object-detection Object Detection +1

Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision

no code implementations23 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.

Feature Correlation Multi-Label Image Classification

Multiple Anchor Learning for Visual Object Detection

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.

General Classification Multiple Instance Learning +3

Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model

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.

Object Object Discovery +5

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