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arXiv · 2308.06947

Knowing Where to Focus: Event-aware Transformer for Video Grounding

Jinhyun Jang·Jungin Park·Jin Kim·Hyeongjun Kwon·Kwanghoon Sohn
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Abstract

Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries. However, their input-agnostic moment queries inevitably overlook an intrinsic temporal structure of a video, providing limited positional information. In this paper, we formulate an event-aware dynamic moment query to enable the model to take the input-specific content and positional information of the video into account. To this end, we present two levels of reasoning: 1) Event reasoning that captures distinctive event units constituting a given video using a slot attention mechanism; and 2) moment reasoning that fuses the moment queries with a given sentence through a gated fusion transformer layer and learns interactions between the moment queries and video-sentence representations to predict moment timestamps. Extensive experiments demonstrate the effectiveness and efficiency of the event-aware dynamic moment queries, outperforming state-of-the-art approaches on several video grounding benchmarks.

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Jinhyun Jang, Jungin Park, Jin Kim, Hyeongjun Kwon, Kwanghoon Sohn. 2023-08-14. Knowing Where to Focus: Event-aware Transformer for Video Grounding. https://arxiv.org/abs/2308.06947

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