Papers › Short-term anchor linking and long-term self-guided attention for video object detection
Short-term anchor linking and long-term self-guided attention for video object detection
Daniel Cores, Víctor M Brea, Manuel Mucientes
We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal information. This module takes advantage of geometrical features in the long-term for the first time in the video object detection domain. Finally, a spatio-temporal double head is fed with both spatial information from the reference frame and the aggregated information that takes into account the short- and long-term temporal context. We have tested our proposal in five video object detection datasets with very different characteristics, in order to prove its robustness in a wide number of scenarios. Non-parametric statistical tests show that our approach outperforms the state-of-the-art. Our code is available at https://github.com/daniel-cores/SLTnet.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Object Detection | ImageNet VID | SLTnet FPN-X101 | MAP | 82.4 | #24 of 33 | Archive leaderboard | report |
| Video Object Detection | USC-GRAD-STDdb | SLTnet FPN-X101 | AP | 16.6 | #1 of 1 | Archive leaderboard | report |
| Video Object Detection | USC-GRAD-STDdb | SLTnet FPN-X101 | AP 0.5 | 44.9 | #1 of 1 | 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.
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