Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition

By Tianshui Chen, Muxin Xu, Xiaolu Hui, Hefeng Wu, Liang Lin. IEEE International Conference on Computer Vision, 2019

356 citations recorded by the source corpus.

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. However, current methods cannot locate the semantic regions accurately due to the lack of part-level supervision or semantic guidance. Moreover, they cannot fully explore the mutual interactions among the semantic regions and do not explicitly model the label co-occurrence. To address these issues, we propose a Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of two crucial modules: 1) a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and 2) a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism. Extensive experiments on public benchmarks show that our SSGRL framework outperforms current state-of-the-art methods by a sizable margin, e.g. with an mAP improvement of 2.5%, 2.6%, 6.7%, and 3.1% on the PASCAL VOC 2007 & 2012, Microsoft-COCO and Visual Genome benchmarks, respectively. Our codes and models are available at https://github.com/HCPLab-SYSU/SSGRL.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/33c67a2a22b807ce620cd3f4dc3c2d37b94014c5

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Citations in the corpus

The Emerging Trends of Multi-Label Learning (2020, background): “Similarly, Semantic-Specific Graph Representation Learning (SSGRL) [158] includes semantic decoupling and interaction modules to learn and correlate semantic-specific representations respectively.”
Asymmetric Loss For Multi-Label Classification (2020, background): “0 SSGRL [4] 93.”
General Multi-label Image Classification with Transformers (2020, methodology): “Following [8, 7], training are images resized to 640 × 640 and randomly cropped to 576 × 576 with random horizontal ﬂips.”