WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration
Gong Chen, Chaokun Zhang, Xinyan Zhao

TL;DR
WhisperNet introduces a receiver-centric, bandwidth-aware framework for collaborative perception that dynamically allocates communication resources, significantly improving efficiency and performance in autonomous driving scenarios.
Contribution
It proposes a novel global coordination paradigm with lightweight metadata and dynamic feature routing, outperforming fixed-rate and object-centric methods.
Findings
Achieves 2.4% higher [email protected] on OPV2V with only 0.5% of full bandwidth.
Boosts baseline performance using just 5% of bandwidth.
Maintains robustness under localization noise.
Abstract
Collaborative perception is vital for autonomous driving yet remains constrained by tight communication budgets. Earlier work reduced bandwidth by compressing full feature maps with fixed-rate encoders, which adapts poorly to a changing environment, and it further evolved into spatial selection methods that improve efficiency by focusing on salient regions, but this object-centric approach often sacrifices global context, weakening holistic scene understanding. To overcome these limitations, we introduce \textit{WhisperNet}, a bandwidth-aware framework that proposes a novel, receiver-centric paradigm for global coordination across agents. Senders generate lightweight saliency metadata, while the receiver formulates a global request plan that dynamically budgets feature contributions across agents and features, retrieving only the most informative features. A collaborative feature…
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Taxonomy
TopicsFace recognition and analysis · Advanced Neural Network Applications · Multimodal Machine Learning Applications
