SwiftReTaKe: Quick and Accurate Redundancy Reduction for Cloud-Edge Collaborative Video-Language Understanding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vision Language Models (VLMs) can enhance Internet of Things (IoT) applications by efficiently extracting valuable information from excessively long videos captured by IoT cameras. Due to the large volume of video data and the high computation overhead of VLMs, a practical deployment strategy is to transmit the video to the cloud only on demand and also deploy the VLMs on the cloud for video analytics. Yet, the interaction experience between humans and VLMs is degraded by the high latency in such cloud-edge collaboration applications. The latency is caused by both the video transmission process and the heavy VLM inference process. We propose SwiftReTaKe, a two-round transmission framework coupled with a low-latency pre-pruning strategy to reduce both network and inference latency. By first sending keyframes for relevance estimation and then adaptively transmitting informative frames, SwiftReTaKe minimizes data transfer and LLM computation. Compared to the state-of-the-art (SOTA) long video processing method, SwiftReTaKe reduces the latency by 6 times with only 3.33% accuracy drop.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it