A Framework for Video-Text Retrieval with Noisy Supervision
Bibliographic record
Abstract
A key challenge in extending vision-linguistic models to new video domains is curating large annotated datasets. We propose a framework that leverages videos with noisy linguistic descriptions, such as sports broadcasts, to train a model using an uncurated dataset. We introduce an unsupervised model that uses the corpus membership between a target and an auxiliary corpus to assign a relevance probability to the linguistic description of examples in the target domain. We examine these probabilities to evaluate the effect of noisy data in the video-text retrieval task. Our framework provides a domain-invariant recipe for enhancing multi-modal datasets by reducing the noise without requiring the costly manual curation effort. We show that our unsupervised model improves the performance of the video-text retrieval model using readily available hockey broadcast videos with closed-captioning. Furthermore, we propose a multi-modal cross-correlation objective function to obtain additional performance gains. We showcase our proposed framework in the context of a new multi-modal dataset of temporally labeled hockey videos with noisy textual descriptions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".