TransCLIP: Transferring Vision–Language Models for Efficient Video Action Recognition
Bibliographic record
Abstract
Transferring contrastive vision-language pretrained models, such as contrastive language–image pretraining (CLIP), to video recognition task has attracted much attention. Recent studies in this area utilized prompt learning within either text or vision branches, or employed an end-to-end CLIP fine-tuning approach. However, these methods do not fully leverage the learning potential of two branches and may compromise zero-shot generalization. In this work, we present a multimodal framework TransCLIP, aiming to adapt vision–language models by integrating both adapter and prompt tuning techniques for the vision and text encoders. Specifically, we incorporate learnable prompt tokens into each transformer encoder layer’s input of vision and text branches, and integrate lightweight adapters into the key and value matrices of the multi-head self-attention modules, enhancing the model’s capability to capture more related video-specific features. To effectively leverage temporal information in videos, we implement a temporal difference attention module (TemDiff attn) that explicitly computes differences between adjacent frame embeddings and conducts difference-level attention to encode motion-related temporal dependency in videos. In addition, a coarse-and-fine contrastive leaning strategy is employed to better align the video and text branches, enhancing the learning capability of the whole framework. Across different evaluation settings, our model consistently outperforms previous State-of-the-Art methods on several video action recognition benchmarks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".