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Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization

arXiv 2105.04967 ↗First posted 11 May 2021cs.CVcited by 0

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Abstract (arXiv)

Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work presents an end-to-end model based on attention-based GCN and semantic matching optimization, which first employs the attention mechanism to enable the central node to learn more discriminating representations from its neighbors in the knowledge graph. Moreover, a coarse-to-fine semantic matching optimization approach is proposed to progressively bridge the domain gap. Experimental results validate that the proposed model not only has superiority on recognizing the images of known and unknown classes, but also can adapt to various openness of the target domain.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 11 May 2021.

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References · 20

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8 more
  1. 2018Open Set Domain Adaptation by Backpropagationcited by 150
  2. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  3. 2018Rethinking Knowledge Graph Propagation for Zero-Shot Learningcited by 87
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  7. 2018An Attention-Based Approach for Single Image Super Resolutioncited by 3
  8. 2020Collaborative Training of Balanced Random Forests for Open Set Domain Adaptationcited by 1

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