Support-set based Multi-modal Representation Enhancement for Video Captioning

• URL: http://arxiv.org/abs/2205.09307v1
• Date: Thu, 19 May 2022 03:40:29 GMT
• Title: Support-set based Multi-modal Representation Enhancement for Video Captioning
• Authors: Xiaoya Chen, Jingkuan Song, Pengpeng Zeng, Lianli Gao and Heng Tao Shen
• Abstract summary: We propose a Support-set based Multi-modal Representation Enhancement (SMRE) model to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way.
• Score: 121.70886789958799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video captioning is a challenging task that necessitates a thorough comprehension of visual scenes. Existing methods follow a typical one-to-one mapping, which concentrates on a limited sample space while ignoring the intrinsic semantic associations between samples, resulting in rigid and uninformative expressions. To address this issue, we propose a novel and flexible framework, namely Support-set based Multi-modal Representation Enhancement (SMRE) model, to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way. Extensive experiments on MSVD and MSR-VTT datasets demonstrate that our SMRE achieves state-of-the-art performance.

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