Spot the Difference! Temporal Coarse to Fine to Finer Difference Spotting for Action Recognition in Videos
Bibliographic record
Abstract
In this paper, we present a novel difference-spotting strategy for video action recognition inspired by the cognitive challenges posed by the childhood puzzle game "Spot the Difference". Our approach aims to enhance the model’s capability to capture time-series variation and intricate details by gradually integrating distinctive information between action and non-action segments in a temporal "coarse-to-fine-to-finer" manner within a discriminative learning framework. To achieve this, we propose a model-agnostic discriminative learning mechanism that can be easily integrated into existing action recognition networks. Firstly, we incorporate coarse-level discriminative information of action and non-action segments across all videos in a corpus using novel booster nets. Secondly, we introduce a fine-level discrimination objective in the penultimate layer of the network through a novel contrastive learning approach, increasing the distinction between different segments within the same video. Lastly, we incorporate finer discrimination through a novel clip matching mechanism, enhancing the distinction of different consecutive clips within an action segment. Experimental results on multiple benchmark datasets (ActivityNet, HACS, FineAction) and backbone architectures (TSN, TSM, TANet, TPN, Timesformer, VideoSwin) demonstrate the effectiveness of our proposed mechanism. We consistently achieve significant improvements (0.33 - 4%) over the baselines, with competitive single crop results on ActivityNet (87.9%) and HACS (90.21%) datasets. Moreover, our technique achieves stateof-the-art classifier results (94.8%) in the ActivityNet 2022 challenge’s validation set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".