australian national university,

No.23-04 Multi-Domain Few-Shot Image Classification

Yadan Luo Follow Mar 15, 2023 · 1 min read
No.23-04 Multi-Domain Few-Shot Image Classification
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Most existing few-shot classification methods only consider generalization on one dataset (i.e., single-domain), failing to transfer across various seen and unseen domains. In this talk, I will introduce the more realistic multi-domain few-shot classification problem to investigate the cross-domain generalization. Specifically, I will elaborate our ICCV 2021 work which designed a parameter-efficient multi-mode modulator (tri-M) to solve the above problem. First, the modulator is designed to maintain multiple modulation parameters (one for each domain) in a single network, thus achieving single-network multi-domain representation. Given a particular domain, domain-aware features can be efficiently generated with the well-devised separative selection and cooperative query modules. Second, we further divide the modulation parameters into the domain-specific set and the domain-cooperative set to explore the intra-domain information and inter-domain correlations, respectively. We demonstrate that the proposed multi-mode modulator achieves state-of-the-art results on the challenging META-DATASET benchmark, especially for unseen test domains.

Short Bio

Dr. Yanbin Liu is currently a Research Fellow in the School of Computing, Australian National University. His research interest involves few-shot learning, deep declarative networks, and spatial-temporal modeling. He has obtained 900+ Google citations, among which the ICLR 19 paper set up a new transductive few-shot benchmark and attracted 600+ followup works. He is the reviewer of major computer vision and machine learning conferences and journals, and received the outstanding reviewer award in CVPR 202

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