模块化量化感知训练用于6D物体姿态估计
Modular Quantization-Aware Training for 6D Object Pose Estimation
AI总结：
针对边缘设备上6D姿态估计网络过大问题，提出模块化量化感知训练（MQAT），利用架构模块化结构实现混合精度量化，在缩小模型4倍以上的同时提升精度超过7%。
摘要：
边缘应用，如协作机器人和航天器交会，要求在资源受限的嵌入式平台上进行高效的6D物体姿态估计。现有的6D姿态估计网络往往过大，难以部署于此类环境，因此需要在保持可靠性能的同时进行压缩。为应对这一挑战，我们提出了模块化量化感知训练（MQAT），这是一种自适应且混合精度的量化感知训练策略，利用现代6D姿态估计架构的模块化结构。MQAT引导系统性的渐进式模块化量化序列，并确定各模块特定的位精度，从而生成的量化模型优于最先进的统一和混合精度量化技术所生成的模型。我们的实验展示了MQAT在数据集、架构和量化算法上的通用性。值得注意的是，经过MQAT训练的量化模型相比基线全精度网络实现了显著的精度提升（>7%），同时将模型大小缩减了4倍或更多。我们的项目网站位于：https://saqibjaved1.github.io/MQAT_/
英文摘要：
Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D pose estimation networks are often too large for such deployments, necessitating compression while maintaining reliable performance. To address this challenge, we introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy that exploits the modular structure of modern 6D pose estimation architectures. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques. Our experiments showcase the generality of MQAT across datasets, architectures, and quantization algorithms. Remarkably, MQAT-trained quantized models achieve a significant accuracy boost (>7%) over the baseline full-precision network while reducing model size by a factor of 4x or more. Our project website is at: https://saqibjaved1.github.io/MQAT_/
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。
