Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains

Brown University
*Indicates Equal Contribution
arXiv Code (coming soon)


Learning from videos is an emerging research area that enables robots to acquire skills from human demonstrations, such as procedural videos. To do this, video-language models must be able to obtain structured understandings, such as the temporal segmentation of a demonstration into sequences of actions and skills, and to generalize the understandings to novel domains. In pursuit of this goal, we introduce Spacewalk-18, a benchmark containing two tasks: (1) step recognition and (2) intra-video retrieval over a dataset of temporally segmented and labeled tasks in International Space Station spacewalk recordings. In tandem, the two tasks quantify a model's ability to make use of: (1) out-of-domain visual information; (2) a high temporal context window; and (3) multimodal (text + video) domains. This departs from existing benchmarks for procedural video understanding, which typically deal with short context lengths and can be solved with a single modality. Spacewalk-18, with its inherent multimodal and long-form complexity, exposes the high difficulty of task recognition and segmentation. We find that state-of-the-art methods perform poorly on our benchmark, demonstrating that the goal of generalizable procedural video understanding models is far out and underscoring the need to develop new approaches to these tasks. Data, model, and code will be publicly released.

Spacewalk-18 Dataset


Spacewalk-18 annotates 18 spacewalk videos from 2019 to 2023 with a total length of 96 hours. On average, each spacewalk task consists of 25 steps. Each step has an average of 12 minutes of animation video.


Two multimodal long-form video understanding tasks are defined on Spacewalk-18 - step recognition and intra-video retrieval.

Step recognition. Given a timestamp and a context window length, step recognition aims to recognize the task step that the timestamp belongs to.

Intra-video retrieval. Given a query timestamp, two candidate timestamps with the same time distances to the query, and a context window length, intra-video retrieval aims to determine the candidate that belongs to the same task step as the query.


Evaluation Results

We evaluate a few pretrained models in both zero-shot and last-layer fine-tuning scenarios. All the models perform poorly on our tasks and significantly worse than humans.


        title={Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains}, 
        author={Rohan Myer Krishnan and Zitian Tang and Zhiqiu Yu and Chen Sun},