AdaCLIP: Towards Pragmatic Multimodal Video Retrieval
Bibliographic record
Abstract
Incorporating large image-text foundation models such as CLIP has substantially improved the performance of the multimodal video retrieval task. However, how to practically sample the frames from a video and aggregate the frame features into a video representation is still an open research question. In particular, real-world deployment scenarios, such as embodiment within consumer electronics or cloud-based inference pipelines, require two key facets of retrieval (representation building and search) to be computationally light and fast. In this paper, we propose AdaCLIP, a computation- and latency-aware system for pragmatic multimodal video retrieval. AdaCLIP consists of a learning-based frame selection module to select informative frames and a query-independent frame aggregation module to obtain strong video representations from the frame features. Specifically, in the frame selection module, we introduce a differentiable Hard-Top-k algorithm to sample a subset of the frames while optimizing the performance of the video retrieval task in an end-to-end manner. Moreover, to be latency-aware, we also propose a query-independent lightweight approach, MLP-Score, to aggregate the frame features into the video representation, which offers up to 142x speedup on GPU and 822x speedup on CPU in similarity search time compared to query-dependent matching methods. Experimental results on several popular video retrieval datasets confirm the effectiveness of AdaCLIP.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".