2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-7.1
Paper Title CROSS-MODAL KNOWLEDGE DISTILLATION FOR FINE-GRAINED ONE-SHOT CLASSIFICATION
Authors Jiabao Zhao, Xin Lin, East China Normal University, China; Yifan Yang, Transwarp Technology, China; Jing Yang, Liang He, East China Normal University, China
SessionMMSP-7: Multimodal Perception, Integration and Multisensory Fusion
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Human Centric Multimedia
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Few-shot learning can recognize a novel category based on only a few samples because it learns to learn from a lot of labeled samples during the training process. When data is insufficient, the performance is affected. And it is expensive to obtain a large-scale fine-grained dataset with annotation. In this paper, we adopt domain-specific knowledge to fill the gap of insufficient annotated data. We propose a cross-modal knowledge distillation (CMKD) framework to do fine-grained one-shot classification and propose the Spatial Relation Loss (SRL) to transfer cross-modal information, which can tackle the semantic gap between multimodal features. The teacher network distills the spatial relationship of the samples as a soft target for training a unimodal student network. Notably, the student network makes predictions only based on a few samples without any external knowledge in the application. This model-agnostic framework will be well adapted to other few-shot models. Extensive experimental results on benchmarks demonstrate that CMKD can make full use of cross-modal knowledge in image and text few-shot classification. CKMD improves the performances of the student networks significantly, even if it is a state-of-the-art student network.