关键时刻用彩色：灰度引导的在线触发用于常开流式视频感知
Color When It Counts: Grayscale-Guided Online Triggering for Always-On Streaming Video Sensing
提出灰度常开、彩色按需的传感范式，利用轻量二次规划在线检测彩色冗余，并配合动态词元路由降低推理成本，为边缘设备上的流式视频理解提供可复用的触发与调度机制。
原文依据 ColorTrigger uses lightweight quadratic programming to detect chromatic redundancy causally, coupled with credit-budgeted control and dynamic token routing
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论常开感知对于下一代边缘/可穿戴AI系统至关重要，然而持续的高保真RGB视频采集对于资源受限的移动和边缘平台而言仍然成本过高。我们提出一种高效流式视频理解的新范式：灰度常开、彩色按需。通过初步研究，我们发现颜色并非总是必要。当通过连续灰度流保留时序结构时，稀疏的RGB帧足以达到可比性能。基于这一洞见，我们提出ColorTrigger，一种无需训练的在线触发器，基于窗口化灰度亲和度分析选择性地激活彩色采集。为实时边缘部署设计，ColorTrigger使用轻量二次规划以因果方式检测彩色冗余，并结合信用预算控制和动态词元路由来共同降低传感和推理成本。在流式视频理解基准上，ColorTrigger仅使用8.1%的RGB帧即达到全彩色基线性能的91.6%，展示了自然视频中大量的颜色冗余，并使资源受限设备上的实用常开视频感知成为可能。
英文原文摘要 1211 字符
Always-on sensing is essential for next-generation edge/wearable AI systems, yet continuous high-fidelity RGB video capture remains prohibitively expensive for resource-constrained mobile and edge platforms. We present a new paradigm for efficient streaming video understanding: grayscale-always, color-on-demand. Through preliminary studies, we discover that color is not always necessary. Sparse RGB frames suffice for comparable performance when temporal structure is preserved via continuous grayscale streams. Building on this insight, we propose ColorTrigger, an online training-free trigger that selectively activates color capture based on windowed grayscale affinity analysis. Designed for real-time edge deployment, ColorTrigger uses lightweight quadratic programming to detect chromatic redundancy causally, coupled with credit-budgeted control and dynamic token routing to jointly reduce sensing and inference costs. On streaming video understanding benchmarks, ColorTrigger achieves 91.6% of full-color baseline performance while using only 8.1% RGB frames, demonstrating substantial color redundancy in natural videos and enabling practical always-on video sensing on resource-constrained devices.