Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains

• URL: http://arxiv.org/abs/2107.05622v1
• Date: Mon, 12 Jul 2021 17:57:46 GMT
• Title: Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
• Authors: Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Vineeth N Balasubramanian, Fahad Shahbaz Khan, Ling Shao
• Abstract summary: We propose a novel approach that learns domain-agnostic structured latent embeddings by projecting images from different domains. Our experiments on the challenging DomainNet and DomainNet-LS benchmarks show the superiority of our approach over existing methods.
• Score: 108.11746235308046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively. However, real-world applications often do not have constrained settings and necessitate handling unseen classes in unseen domains -- a setting called Zero-shot Domain Generalization, which presents the issues of domain and semantic shifts simultaneously. In this work, we propose a novel approach that learns domain-agnostic structured latent embeddings by projecting images from different domains as well as class-specific semantic text-based representations to a common latent space. In particular, our method jointly strives for the following objectives: (i) aligning the multimodal cues from visual and text-based semantic concepts; (ii) partitioning the common latent space according to the domain-agnostic class-level semantic concepts; and (iii) learning a domain invariance w.r.t the visual-semantic joint distribution for generalizing to unseen classes in unseen domains. Our experiments on the challenging DomainNet and DomainNet-LS benchmarks show the superiority of our approach over existing methods, with significant gains on difficult domains like quickdraw and sketch.

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