Multi-Label Image Recognition
19 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Label Image Recognition
Most implemented papers
Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Kevinz-code/CSRA
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ICCV 2021
Multi-label image recognition is a challenging computer vision task of practical use.
Multi-Label Image Recognition with Graph Convolutional Networks
megvii-research/ml-gcn
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CVPR 2019
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
HCPLab-SYSU/SSGRL
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ICCV 2019
Recognizing multiple labels of images is a practical and challenging task, and significant progress has been made by searching semantic-aware regions and modeling label dependency.
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
gaobb/MCAR
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To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.
Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition
Yejin0111/ADD-GCN
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ECCV 2020
To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image.
Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
Kso-Ayaka/MS_Paperwithcode
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IEEE Transactions on Pattern Analysis and Machine Intelligence 2021
The task of multi-label image recognition is to predict a set of object labels that present in an image.
M3TR: Multi-modal Multi-label Recognition with Transformer
Multi-label image recognition aims to recognize multiple objects simultaneously in one image.
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i. e., merely some labels are known while other labels are missing (also called unknown labels) per image.
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion.
