Structured Semantic Transfer for Multi-Label Recognition with Partial Labels

• URL: http://arxiv.org/abs/2112.10941v2
• Date: Wed, 22 Dec 2021 01:40:19 GMT
• Title: Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
• Authors: Tianshui Chen, Tao Pu, Hefeng Wu, Yuan Xie, Liang Lin
• Abstract summary: We propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations. Experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms.
• Score: 85.6967666661044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. 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. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.

Related papers

• PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification [48.929583521641526]
  Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper introduces the conditional transport theory to bridge the acknowledged gap. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently.
  arXiv  Detail & Related papers  (2023-07-18T08:37:37Z)
• Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition [21.36538164675385]
  Multi-label image recognition aims to predict a set of labels that present in an image. 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. We propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM)
  arXiv  Detail & Related papers  (2023-04-21T23:48:01Z)
• Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [70.36722026729859]
  We propose a dual-perspective semantic-aware representation blending (DSRB) that blends multi-granularity category-specific semantic representation across different images. The proposed DS consistently outperforms current state-of-the-art algorithms on all proportion label settings.
  arXiv  Detail & Related papers  (2022-05-26T00:33:44Z)
• Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels [70.45813147115126]
  Multi-label image recognition with partial labels (MLR-PL) may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find that strong semantic correlations exist within each image and across different images. These correlations can help transfer the knowledge possessed by the known labels to retrieve the unknown labels.
  arXiv  Detail & Related papers  (2022-05-23T08:37:38Z)
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [86.17081952197788]
  We propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels. Experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors.
  arXiv  Detail & Related papers  (2022-03-04T07:56:16Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification [25.96221714337815]
  Domain adaptive person re-identification (re-ID) is a challenging task due to the large discrepancy between the source domain and the target domain. Existing methods mainly attempt to generate pseudo labels for unlabeled target images by clustering algorithms. We propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning.
  arXiv  Detail & Related papers  (2020-09-13T10:12:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.