Multi-Label Image Recognition

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (37 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Residual Attention: A Simple but Effective Method for Multi-Label Recognition
  5 Aug 2021 5 repositories listed
  Multi-label image recognition is a challenging computer vision task of practical use.
• Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
  20 Aug 2019 2 repositories listed
  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.
• Multi-Label Image Recognition with Graph Convolutional Networks
  7 Apr 2019 2 repositories listed
  The task of multi-label image recognition is to predict a set of object labels that present in an image.
• Task Singular Vectors: Reducing Task Interference in Model Merging
  26 Nov 2024 1 repository listed
  In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular value decomposition.
• Text-Region Matching for Multi-Label Image Recognition with Missing Labels
  26 Jul 2024 1 repository listed
  Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual…
• Learning to recognize occluded and small objects with partial inputs
  27 Oct 2023 1 repository listed
  Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small.
• GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
  28 Aug 2023 1 repository listed
  Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image while modeling the complex relationships between labels and image regions.
• Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
  15 Jul 2023 1 repository listed
  Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets.
• Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
  21 Apr 2023 1 repository listed
  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.
• Texts as Images in Prompt Tuning for Multi-Label Image Recognition
  23 Nov 2022 1 repository listed
  Nonetheless, visual data (e.
• Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
  26 May 2022 1 repository listed
  Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in…
• Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
  23 May 2022 1 repository listed
  Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.
• AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
  10 Mar 2022 1 repository listed
  The key challenges of LML image recognition are the construction of label relationships on Partial Labels of training data and the Catastrophic Forgetting on old classes, resulting in poor generalization.
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
  4 Mar 2022 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion.
• Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
  21 Dec 2021 1 repository listed
  To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.
• Transformer-based Dual Relation Graph for Multi-label Image Recognition
  10 Oct 2021 1 repository listed Syntology ran 9 of 12 samples · 3 unverified
  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.
• M3TR: Multi-modal Multi-label Recognition with Transformer
  1 Oct 2021 1 repository listed
  Multi-label image recognition aims to recognize multiple objects simultaneously in one image.
• Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
  3 Mar 2021 1 repository listed
  The task of multi-label image recognition is to predict a set of object labels that present in an image.
• Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition
  5 Dec 2020 1 repository listed
  To this end, we propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image.
• Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
  3 Jul 2020 1 repository listed
  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…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.