1 code implementation • 15 Dec 2023 • Yanan Wu, Zhixiang Chi, Yang Wang, Konstantinos N. Plataniotis, Songhe Feng
In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer.
1 code implementation • ICCV 2023 • Yanan Wu, Zhixiang Chi, Yang Wang, Songhe Feng
The goal is to continually discover novel classes while maintaining the performance in known classes.
1 code implementation • 17 Jul 2023 • Tengfei Liang, Yi Jin, Wu Liu, Tao Wang, Songhe Feng, Yidong Li
Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras.
Cross-Modality Person Re-identification Image Classification +4
no code implementations • 10 May 2023 • Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen
In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels.
1 code implementation • 21 Apr 2023 • Yanan Wu, Songhe Feng, Yang Wang
In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem.
no code implementations • ICCV 2023 • Jintian Ji, Songhe Feng
Specifically, an anchor representation tensor is constructed by using the anchor representation strategy rather than the self-representation strategy to reduce the time complexity, and an Anchor Structure Regularization (ASR) is employed to enhance the local geometric structure in the learned anchor-representation tensor.
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 • 18 Oct 2021 • Tengfei Liang, Yi Jin, Yajun Gao, Wu Liu, Songhe Feng, Tao Wang, Yidong Li
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance.
Cross-Modality Person Re-identification Person Re-Identification
no code implementations • 30 Apr 2021 • Yanan Wu, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, Zizhang Wu
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image.
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 • 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 • 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 • 20 Apr 2018 • Gengyu Lyu, Songhe Feng, Congyang Lang
Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label.
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.