FRESHLATENT：面向资源受限具身VLM感知的信道感知潜在适配
FRESHLATENT: Channel-Aware Latent Adaptation for Resource-Constrained Embodied VLM Perception
- University of California, Irvine（加州大学欧文分校）
- Kookmin University（国民大学）
- Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对资源受限无人机分割VLM感知中无线信道损坏导致部署失配的问题，提出轻量级信道感知适配器FreshLatent，以极低开销恢复大部分鲁棒性，显著提升恶劣信道下的感知质量。
AI中文摘要：
关键任务无人机在机载资源和无线通信严格受限的条件下，日益依赖分割式视觉语言模型（VLM）感知。然而，传输中间特征的损坏会导致干净训练的分割接口出现部署失配，而更强的信道感知编解码器则会带来可观机载成本。我们提出FreshLatent，一种轻量级信道感知潜在适配器，它通过无线损坏训练一个功率归一化的编码器-解码器，同时保持周围的VLM冻结。我们围绕任务条件感知需求和嵌入式接口成本进行部署公式化，将信道质量和通信预算与感知保持可用的运行条件联系起来。在0 dB和最紧通信预算下，FreshLatent相比干净分割压缩分别将gIoU和cIoU提升了20.79和20.87个百分点。在最不利的评估SNR（0 dB）下，跨所有三个通信预算，FreshLatent恢复了由更重的、范围训练的feature-JSCC编解码器所实现的gIoU改进的63.5-69.1%。在NVIDIA Jetson AGX Xavier的10-W模式下，FreshLatent相比更重编解码器使用37-40倍更少的编码器参数，7.7-9.9倍更低的边缘接口延迟，以及8.8-10.0倍更低的边缘接口能耗。综合这些结果表明，轻量级信道感知适配可以恢复更大通信接口鲁棒性的相当大一部分，同时在受限无线条件下拓宽质量有效的运行范围。
英文摘要：
Mission-critical UAVs increasingly rely on split vision-language model (VLM) perception under tight onboard-resource and wireless-communication constraints. However, corruption of transmitted intermediate features creates a deployment mismatch for clean-trained split interfaces, while stronger channel-aware codecs can impose substantial onboard cost. We present FreshLatent, a lightweight channel-aware latent adapter that trains a power-normalized encoder-decoder through wireless corruption while keeping the surrounding VLM frozen. We formulate deployment around a mission-conditioned perception requirement and embedded interface cost, linking channel quality and communication budget to the operating conditions under which perception remains usable. At 0 dB and the tightest communication budget, FreshLatent improves gIoU and cIoU over clean split compression by 20.79 and 20.87 points, respectively. At the most adverse evaluated SNR (0 dB), across all three communication budgets, FreshLatent recovers 63.5-69.1% of the gIoU improvement achieved by a much heavier, range-trained feature-JSCC codec. On an NVIDIA Jetson AGX Xavier in 10-W mode, FreshLatent uses 37-40x fewer encoder parameters, 7.7-9.9x lower edge-interface latency, and 8.8-10.0x lower edge-interface energy than the heavier codec. Together, these results show that lightweight channel-aware adaptation can recover a substantial fraction of the robustness of a much larger communication interface while broadening quality-valid operation under constrained wireless conditions.
