1 code implementation • 17 Mar 2023 • Gongpei Zhao, Tao Wang, Yidong Li, Yi Jin, Congyan Lang, Haibin Ling
Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning.
1 code implementation • 16 Jan 2023 • Yushan Han, HUI ZHANG, Huifang Li, Yi Jin, Congyan Lang, Yidong Li
The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 11 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.
no code implementations • 21 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
2 code implementations • 1 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.
no code implementations • 26 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.
2 code implementations • 30 Oct 2020 • Tengfei Liang, Yi Jin, Yidong Li, Tao Wang, Songhe Feng, Congyan Lang
In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN).
Ranked #1 on Denoising on AAPM
no code implementations • 25 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.
no code implementations • 3 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.
no code implementations • 25 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.
no code implementations • 24 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.
no code implementations • 10 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.
no code implementations • ICCV 2017 • Zhu Teng, Junliang Xing, Qiang Wang, Congyan Lang, Songhe Feng, Yi Jin
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
2 code implementations • 19 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.
Ranked #3 on Multi-Human Parsing on MHP v1.0