Papers › Temporal Reasoning Graph for Activity Recognition
Temporal Reasoning Graph for Activity Recognition
Jingran Zhang, Fumin Shen, Xing Xu, Heng Tao Shen
Despite great success has been achieved in activity analysis, it still has many challenges. Most existing work in activity recognition pay more attention to design efficient architecture or video sampling strategy. However, due to the property of fine-grained action and long term structure in video, activity recognition is expected to reason temporal relation between video sequences. In this paper, we propose an efficient temporal reasoning graph (TRG) to simultaneously capture the appearance features and temporal relation between video sequences at multiple time scales. Specifically, we construct learnable temporal relation graphs to explore temporal relation on the multi-scale range. Additionally, to facilitate multi-scale temporal relation extraction, we design a multi-head temporal adjacent matrix to represent multi-kinds of temporal relations. Eventually, a multi-head temporal relation aggregator is proposed to extract the semantic meaning of those features convolving through the graphs. Extensive experiments are performed on widely-used large-scale datasets, such as Something-Something and Charades, and the results show that our model can achieve state-of-the-art performance. Further analysis shows that temporal relation reasoning with our TRG can extract discriminative features for activity recognition.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | Something-Something V1 | TRG (Inception-V3) | Top 1 Accuracy | 49.7 | #55 of 74 | Archive leaderboard | report |
| Action Recognition | Something-Something V1 | TRG (ResNet-50) | Top 1 Accuracy | 49.5 | #56 of 74 | Archive leaderboard | report |
| Action Recognition | Something-Something V1 | TRG (ResNet-50) | Top 5 Accuracy | 86.1 | #56 of 74 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | TRG (ResNet-50) | Top-1 Accuracy | 62.2 | #105 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | TRG (ResNet-50) | Top-5 Accuracy | 90.3 | #105 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | TRG (Inception-V3) | Top-1 Accuracy | 61.3 | #109 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | TRG (Inception-V3) | Top-5 Accuracy | 91.4 | #109 of 123 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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