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Learning Dual Low-Rank Representation for Multi-Label Micro-Video Classification by Wei Lu; Desheng Li; Liqiang Nie; Peiguang Jing; Yuting Su is a Computer Science article available to read on EtoBox.

What is Learning Dual Low-Rank Representation for Multi-Label Micro-Video Classification about?

Currently, with the rapid development of mobile Internet, micro-video has become a prevailing format of user-generated contents (UGCs) on various social media platforms. Several studies have been conducted towards to understanding high-level micro-video semantics, such as venue categorization, memorability, and popularity. However, these approaches supported tasks with only a single output, which exhibited limitations when attempting to use them to resolve tasks with multiple outputs, especially the multi-label microvideo classification. To tackle this problem, in this paper, we propose a dual multi-modal low-rank decomposition (DMLRD) method for multi-label micro-video classification tasks. To learn more comprehensive micro-video representations, we first learn the low-rank-regularized modality-specific and modalityshared components by considering the consistency and the complementarity among modalities simultaneously. Meanwhile, the less descriptive power of each modality aroused by inherent properties can be solved to a certain extent. To obtain unseen label representations, we next construct a sparsity-regularized multi-matrix normal estimation term to jointly encode the latent

Who reads Learning Dual Low-Rank Representation for Multi-Label Micro-Video Classification?

Author

Wei Lu; Desheng Li; Liqiang Nie; Peiguang Jing; Yuting Su

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)