Spatio-Temporal Activity Detection via Joint Optimization of Spatial and Temporal Localization
Bibliographic record
Abstract
In this article, we address the problem of spatiotemporal activity detection which requires classifying as well as localizing human activities both in space and time from videos. To this end, we propose a novel single-stage and end-to-end trainable deep learning framework that can jointly optimize spatial and temporal localization of ac-tivities. Leveraging shared spatiotemporal feature maps, the proposed framework performs actor detection, activity tube building, as well as temporal localization of activities, all within a single network. The proposed framework outperforms the current state-of-the-art methods in spatiotemporal activity detection on the challenging UCF101-24 benchmark. By utilizing solely RGB input, it achieves a video-mAP of 60.1%, and further pushes the bar to 61.3% when incorporating both RGB and FLOW inputs. More-over, it attains a highly competitive frame-mAP of 74.9%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".