Search Results for author: Congyan Lang

Found 14 papers, 3 papers with code

GLAN: A Graph-based Linear Assignment Network

no code implementations5 Jan 2022 He Liu, Tao Wang, Congyan Lang, Songhe Feng, Yi Jin, Yidong Li

The experimental results on a synthetic dataset reveal that our method outperforms state-of-the-art baselines and achieves consistently high accuracy with the increment of the problem size.

Multi-Object Tracking

Deep Probabilistic Graph Matching

no code implementations5 Jan 2022 He Liu, Tao Wang, Yidong Li, Congyan Lang, Songhe Feng, Haibin Ling

Most previous learning-based graph matching algorithms solve the \textit{quadratic assignment problem} (QAP) by dropping one or more of the matching constraints and adopting a relaxed assignment solver to obtain sub-optimal correspondences.

Graph Matching

Clicking Matters:Towards Interactive Human Parsing

no code implementations11 Nov 2021 Yutong Gao, Liqian Liang, Congyan Lang, Songhe Feng, Yidong Li, Yunchao Wei

In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions.

Human Parsing Semantic Segmentation

MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification

no code implementations21 Oct 2021 Yajun Gao, Tengfei Liang, Yi Jin, Xiaoyan Gu, Wu Liu, Yidong Li, Congyan Lang

The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality.

Cross-Modality Person Re-identification Person Re-Identification

Joint Graph Learning and Matching for Semantic Feature Correspondence

2 code implementations1 Sep 2021 He Liu, Tao Wang, Yidong Li, Congyan Lang, Yi Jin, Haibin Ling

In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.

Graph Learning Graph Matching

A Universal Model for Cross Modality Mapping by Relational Reasoning

no code implementations26 Feb 2021 Zun Li, Congyan Lang, Liqian Liang, Tao Wang, Songhe Feng, Jun Wu, Yidong Li

With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community.

Image Classification Relational Reasoning

Cross-layer Feature Pyramid Network for Salient Object Detection

no code implementations25 Feb 2020 Zun Li, Congyan Lang, Junhao Liew, Qibin Hou, Yidong Li, Jiashi Feng

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection.

RGB Salient Object Detection Salient Object Detection

HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

no code implementations3 Jun 2019 Gengyu Lyu, Songhe Feng, Yi Jin, Guojun Dai, Congyan Lang, Yidong Li

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct.

Partial Label Learning

Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification

no code implementations25 May 2019 Yangru Huang, Peixi Peng, Yi Jin, Junliang Xing, Congyan Lang, Songhe Feng

To reduce domain divergence caused by that the source and target datasets are collected from different environments, we force to project the DSH feature maps from different domains to a new nominal domain, and a novel domain similarity loss is proposed based on one-class classification.

Domain Adaptation General Classification +1

Deep Reasoning with Multi-Scale Context for Salient Object Detection

no code implementations24 Jan 2019 Zun Li, Congyan Lang, Yunpeng Chen, Junhao Liew, Jiashi Feng

However, the saliency inference module that performs saliency prediction from the fused features receives much less attention on its architecture design and typically adopts only a few fully convolutional layers.

RGB Salient Object Detection Saliency Prediction +1

GM-PLL: Graph Matching based Partial Label Learning

no code implementations10 Jan 2019 Gengyu Lyu, Songhe Feng, Tao Wang, Congyan Lang, Yidong Li

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct.

Graph Matching Partial Label Learning

Multiple-Human Parsing in the Wild

2 code implementations19 May 2017 Jianshu Li, Jian Zhao, Yunchao Wei, Congyan Lang, Yidong Li, Terence Sim, Shuicheng Yan, Jiashi Feng

To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.

Multi-Human Parsing

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