Neptune: The Long Orbit to Benchmarking Long Video Understanding

Arsha Nagrani, Mingda Zhang, Ramin Mehran, Rachel Hornung, Nitesh Bharadwaj Gundavarapu, Nilpa Jha, Austin Myers, Xingyi Zhou, Boqing Gong, Cordelia Schmid, Mikhail Sirotenko, Yukun Zhu, Tobias Weyand

Keywords: video understanding, dataset, metric, long video understanding, benchmark

TL;DR: Automatic pipeline to generate data for long video understanding

Abstract: This paper describes a semi-automatic pipeline to generate challenging question-answer-decoy sets for understanding long videos. Many existing video datasets and models are focused on short clips (10s-30s). While some long video datasets do exist, they can often be solved by powerful image models applied per frame (and often to very few frames) in a video, and are usually manually annotated at high cost. In order to mitigate both these problems, we propose a scalable dataset creation pipeline which leverages large models (VLMs and LLMs), to automatically generate dense, time-aligned video captions, as well as tough question answer decoy sets for video segments (up to 15 minutes in length). Our dataset Neptune covers a broad range of long video reasoning abilities and consists of a subset tha temphasizes multimodal reasoning. Since existing metrics for open-ended question answering are either rule-based or may rely on proprietary models, we provide a new open source model-based metric (GEM) to score open-ended responses on Neptune. Benchmark evaluations reveal that current open-source long video models perform poorly on Neptune, particularly on questions testing temporal ordering, counting and state changes. Through Neptune, we aim to spur the development of more advanced models capable of understanding long videos.

Primary Area: datasets and benchmarks

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Submission Number: 4809