Papers › Compositional Video Understanding with Spatiotemporal Structure-based Transformers
Compositional Video Understanding with Spatiotemporal Structure-based Transformers
Hoyeoung Yun, Jinwoo Ahn, Minseo Kim, Eun-Sol Kim
In this paper we suggest a new novel method to understand complex semantic structures through long video inputs. Conventional methods for understanding videos have been focused on short-term clips and trained to get visual representations for the short clips using convolutional neural networks or transformer architectures. However most real-world videos are composed of long videos ranging from minutes to hours therefore it essentially brings limitations to understanding the overall semantic structures of the long videos by dividing them into small clips and learning the representations of them. We suggest a new algorithm to learn the multi-granular semantic structures of videos by defining spatiotemporal high-order relationships among object-based representations as semantic units. The proposed method includes a new transformer architecture capable of learning spatiotemporal graphs and a compositional learning method to learn disentangled features for each semantic unit. Using the suggested method we resolve the challenging video task which is compositional generalization understanding of unseen videos. In experiments we demonstrate new state-of-the-art performances for two challenging video datasets.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections