SimBase: A Simple Baseline for Temporal Video Grounding
원문 초록
This paper presents SimBase, a simple yet effective baseline for temporal video grounding. While recent advances in temporal grounding have led to impressive performance, they have also driven network architectures toward greater complexity, with a range of methods to (1) capture temporal relationships and (2) achieve effective multimodal fusion. In contrast, this paper explores the question: How effective can a simplified approach be? To investigate, we design SimBase, a network that leverages lightweight, one-dimensional temporal convolutional layers instead of complex temporal structures. For cross-modal interaction, SimBase only employs an element-wise product instead of intricate multimodal fusion. Remarkably, SimBase achieves state-of-the-art results on two large-scale datasets. As a simple yet powerful baseline, we hope SimBase will spark new ideas and streamline future evaluations in temporal video grounding.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency
DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos
Object-Shot Enhanced Grounding Network for Egocentric Video
TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM
OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
토론
이 논문과 연결된 포럼 글이 아직 없습니다.