Localizing Moments in Long Video Via Multimodal Guidance

• DOI: 10.48550/arxiv.2302.13372
• Publication Date: 2023-10-01
• Authors: Barrios, Wayner; Soldan, Mattia; Ceballos-Arroyo, ...; Heilbron, Fabian ...; Ghanem, Bernard
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; 0209 industrial biotechnology; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Machine Learning (cs.LG)

The recent introduction of the large-scale, long-form MAD and Ego4D datasets has enabled researchers to investigate the performance of current state-of-the-art methods for video grounding in the long-form setup, with interesting findings: current grounding methods alone fail at tackling this challenging task and setup due to their inability to process long video sequences. In this paper, we propose a method for improving the performance of natural language grounding in long videos by identifying and pruning out non-describable windows. We design a guided grounding framework consisting of a Guidance Model and a base grounding model. The Guidance Model emphasizes describable windows, while the base grounding model analyzes short temporal windows to determine which segments accurately match a given language query. We offer two designs for the Guidance Model: Query-Agnostic and Query-Dependent, which balance efficiency and accuracy. Experiments demonstrate that our proposed method outperforms state-of-the-art models by 4.1% in MAD and 4.52% in Ego4D (NLQ), respectively. Code, data and MAD's audio features necessary to reproduce our experiments are available at: https://github.com/waybarrios/guidance-based-video-grounding.