让车载感知共享更高效，把背景信息压缩进前景特征里
Background Fades, Foreground Leads: Curriculum-Guided Background Pruning for Efficient Foreground-Centric Collaborative Perception
- 用课程学习策略逐步剪裁背景，让模型学会在前景中内化上下文
- 在不同带宽下均优于现有方法，提升长尾场景感知可靠性
- 适合自动驾驶协同感知、带宽受限场景下的系统设计
协同感知通过车辆间共享互补信息，提升了自动驾驶的可靠性和空间覆盖范围，是应对单车感知难以覆盖的长尾场景的可行方案。然而，车联网带宽有限，直接传输完整特征图不现实。近期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，舍弃背景，但背景蕴含重要上下文信息。我们提出FadeLead框架，通过训练使模型将背景上下文编码到紧凑的前景特征中。核心是课程学习策略：早期利用背景线索，逐步剪裁背景，迫使模型在不传输背景的前提下，将上下文内化至前景表示。在模拟与真实世界基准上的大量实验表明，FadeLead在不同带宽设置下均优于现有方法，验证了上下文丰富前景共享的有效性。
原文摘要 · Abstract (English)
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.
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