Communication-Efficient Multi-Agent Collaborative Perception via Spatio-Temporal Heterogeneity
Bibliographic record
Abstract
Multi-agent collaborative perception enables a single agent to perceive the comprehensive scene by exchanging sensory information using vehicle-to-everything communication. However, the communication resources of real-world communication systems are often limited. It fails to satisfy the real-time transmission of extensive data, which restricts the deployment of collaborative perception. To address the issue, we propose ComSH, a Communication-efficient multi-agent collaborative perception method based on Spatio-temporal Heterogeneity to achieve a trade-off between performance and bandwidth. Specifically, we consider the spatio-temporal heterogeneity to perform feature filtering, thus reducing the redundancy of transmitted features. First, it introduces valuable temporal semantics to enhance the current representation of each agent. Secondly, we consider the spatial confidence of each agent at each location and the relative position to obtain high confidence and complementary features. Eventually, a foreground object adaptive activation module is designed to enrich the visual representation of fused features. Therefore, ComSH enables different agents to transmit their critical and complementary perception information, thus reducing bandwidth consumption. We conduct experiments on collaborative detection tasks on the two datasets. Experimental results demonstrate the ComSH’s superiority and effectiveness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".