143 citations · 337 across the 14 of their papers we have counts for
14 papers
MetaGCD: Learning to Continually Learn in Generalized Category Discovery
Yanan Wu, Zhixiang Chi, Yang Wang +1
In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel class…
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification
Tengfei Liang, Yi Jin, Wu Liu +3
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.…
Deep Partial Multi-Label Learning with Graph Disambiguation
Haobo Wang, Shisong Yang, Gengyu Lyu +5
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. Rec…
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
Yanan Wu, Songhe Feng, Yang Wang
Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and lab…
GLAN: A Graph-based Linear Assignment Network
He Liu, Tao Wang, Congyan Lang +3
Differentiable solvers for the linear assignment problem (LAP) have attracted much research attention in recent years, which are usually embedded into learning frameworks as compon…
Deep Probabilistic Graph Matching
He Liu, Tao Wang, Yidong Li +3
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 rela…
Clicking Matters:Towards Interactive Human Parsing
Yutong Gao, Liqian Liang, Congyan Lang +3
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. This new task…
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification
Tengfei Liang, Yi Jin, Yajun Gao +4
Visible-infrared cross-modality person re-identification is a challenging ReID task, which aims to retrieve and match the same identity's images between the heterogeneous visible a…
GM-MLIC: Graph Matching based Multi-Label Image Classification
Yanan Wu, He Liu, Songhe Feng +3
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image conten…
A Universal Model for Cross Modality Mapping by Relational Reasoning
Zun Li, Congyan Lang, Liqian Liang +4
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. Existing method…
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
Tengfei Liang, Yi Jin, Yidong Li +3
In the past few decades, to reduce the risk of X-ray in computed tomography (CT), low-dose CT image denoising has attracted extensive attention from researchers, which has become a…
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Gengyu Lyu, Songhe Feng, Yi Jin +3
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. Most existing m…
GM-PLL: Graph Matching based Partial Label Learning
Gengyu Lyu, Songhe Feng, Tao Wang +2
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. The key to deal…
A Self-paced Regularization Framework for Partial-Label Learning
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. Most PLL algori…