Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning

• URL: http://arxiv.org/abs/2312.06699v1
• Date: Sun, 10 Dec 2023 02:03:51 GMT
• Title: Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning
• Authors: Zaber Ibn Abdul Hakim, Najibul Haque Sarker, Rahul Pratap Singh, Bishmoy Paul, Ali Dabouei, Min Xu
• Abstract summary: A thorough comprehension of textual data is a fundamental element in multi-modal video analysis tasks. We postulate that understanding the significance of the sentence components according to the target task can potentially enhance the performance of the models. We propose a weakly supervised importance estimation module to compute the relative importance of the components and utilize them to improve different video-language tasks.
• Score: 10.486585276898472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A thorough comprehension of textual data is a fundamental element in multi-modal video analysis tasks. However, recent works have shown that the current models do not achieve a comprehensive understanding of the textual data during the training for the target downstream tasks. Orthogonal to the previous approaches to this limitation, we postulate that understanding the significance of the sentence components according to the target task can potentially enhance the performance of the models. Hence, we utilize the knowledge of a pre-trained large language model (LLM) to generate text samples from the original ones, targeting specific sentence components. We propose a weakly supervised importance estimation module to compute the relative importance of the components and utilize them to improve different video-language tasks. Through rigorous quantitative analysis, our proposed method exhibits significant improvement across several video-language tasks. In particular, our approach notably enhances video-text retrieval by a relative improvement of 8.3\% in video-to-text and 1.4\% in text-to-video retrieval over the baselines, in terms of R@1. Additionally, in video moment retrieval, average mAP shows a relative improvement ranging from 2.0\% to 13.7 \% across different baselines.

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