2024-04-18-chae24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Towards a Complete Benchmark on Video Moment Localization	https://github.com/snuviplab/MoLEF	In this paper, we propose and conduct a comprehensive benchmark on moment localization task, which aims to retrieve a segment that corresponds to a text query from a single untrimmed video. Our study starts from an observation that most moment localization papers report experimental results only on a few datasets in spite of availability of far more benchmarks. Thus, we conduct an extensive benchmark study to measure the performance of representative methods on widely used 7 datasets. Looking further into the details, we pose additional research questions and empirically verify them, including if they rely on unintended biases introduced by specific training data, if advanced visual features trained on classification task transfer well to this task, and if computational cost of each model pays off. With a series of these experiments, we provide multi-faceted evaluation of state-of-the-art moment localization models. Codes are available at \url{https://github.com/snuviplab/MoLEF}.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chae24a	0	Towards a Complete Benchmark on Video Moment Localization	4168	4176	4168-4176	4168	false	Chae, Jinyeong and Kim, Donghwa and Kim, Kwanseok and Lee, Doyeon and Lee, Sangho and Ha, Seongsu and Mun, Jonghwan and Kang, Wooyoung and Roh, Byungseok and Lee, Joonseok	given family Jinyeong Chae given family Donghwa Kim given family Kwanseok Kim given family Doyeon Lee given family Sangho Lee given family Seongsu Ha given family Jonghwan Mun given family Wooyoung Kang given family Byungseok Roh given family Joonseok Lee	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/chae24a/chae24a.pdf