Summary of Length-aware Detr For Robust Moment Retrieval, by Seojeong Park et al.

Length-Aware DETR for Robust Moment Retrieval

by Seojeong Park, Jiho Choi, Kyungjune Baek, Hyunjung Shim

First submitted to arxiv on: 30 Dec 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper proposes a novel approach to Video Moment Retrieval (MR) that addresses the limitations of recent DETR-based models in accurately localizing short moments. By analyzing feature diversity, the authors identify limited foreground and background features in short moments, which motivates the development of MomentMix, an augmentation strategy that enhances these features. Additionally, the paper reveals prediction bias towards center positions of moments, leading to the proposal of a Length-Aware Decoder (LAD) that conditions length through a novel bipartite matching process. The method is evaluated on benchmark datasets and outperforms state-of-the-art DETR-based methods, achieving improved overall performance and accuracy in localizing short moments.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper is about finding specific moments within videos based on what you say. It’s like searching for a specific scene in a movie or TV show. Right now, computers are not very good at doing this, especially when the moment is short. The researchers looked into why this is and found that there’s a problem with how they’re representing the moment. They created new ways to make these representations better, which helped them find moments more accurately. They also came up with a special way to handle moments that are shorter than others. This made their approach even better at finding short moments.