DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding

• URL: http://arxiv.org/abs/2312.02549v1
• Date: Tue, 5 Dec 2023 07:37:21 GMT
• Title: DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding
• Authors: Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
• Abstract summary: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. We propose an energy-based model framework to explicitly learn moment-query distributions. We also propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor.
• Score: 32.45280955448672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query. Recent advances employ the attention mechanism to learn the relations between video moments and the text query. However, naive attention might not be able to appropriately capture such relations, resulting in ineffective distributions where target video moments are difficult to separate from the remaining ones. To resolve the issue, we propose an energy-based model framework to explicitly learn moment-query distributions. Moreover, we propose DemaFormer, a novel Transformer-based architecture that utilizes exponential moving average with a learnable damping factor to effectively encode moment-query inputs. Comprehensive experiments on four public temporal language grounding datasets showcase the superiority of our methods over the state-of-the-art baselines.

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