背景褪色，前景引领：基于课程学习的背景剪枝用于高效前景导向的协同感知
Background Fades, Foreground Leads: Curriculum-Guided Background Pruning for Efficient Foreground-Centric Collaborative Perception
AI总结：
本文提出FadeLead框架，通过课程学习策略在训练中学习将背景上下文压缩为紧凑的前景特征，提升协同感知的效率和效果。
摘要：
协同感知通过车辆间共享互补信息提升自动驾驶车辆的可靠性和空间覆盖范围，为解决长尾场景提供了解决方案。然而，车辆网络的带宽限制使得传输完整特征图不现实。最近的方法采用以前景为中心的范式，仅传输预测的前景区域特征并丢弃背景，这编码了关键上下文。我们提出FadeLead，一种以前景为中心的框架，通过在训练中学习将背景上下文封装为紧凑的前景特征来克服这一限制。我们的设计核心是一种课程学习策略，早期利用背景提示，逐步剪枝它们，迫使模型将上下文内部化为前景表示，而无需传输背景本身。在模拟和现实世界基准上的广泛实验表明，FadeLead在不同带宽设置下均优于先前方法，证明了富含上下文的前景共享的有效性。
英文摘要：
Collaborative perception enhances the reliability and spatial coverage of autonomous vehicles by sharing complementary information across vehicles, offering a promising solution to long-tail scenarios that challenge single-vehicle perception. However, the bandwidth constraints of vehicular networks make transmitting the entire feature map impractical. Recent methods, therefore, adopt a foreground-centric paradigm, transmitting only predicted foreground-region features while discarding the background, which encodes essential context. We propose FadeLead, a foreground-centric framework that overcomes this limitation by learning to encapsulate background context into compact foreground features during training. At the core of our design is a curricular learning strategy that leverages background cues early on but progressively prunes them away, forcing the model to internalize context into foreground representations without transmitting background itself. Extensive experiments on both simulated and real-world benchmarks show that FadeLead outperforms prior methods under different bandwidth settings, underscoring the effectiveness of context-enriched foreground sharing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
