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 IDMLSP-4.2
Paper Title Cross-Modal Representation Reconstruction for Zero-Shot Classification
Authors Yu Wang, Tongji University, China; Shenjie Zhao, Mojie Technology(Ningbo), China
SessionMLSP-4: Machine Learning for Classification Applications 1
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CMRR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.