面向低延迟视觉语言模型：在自我中心视觉理解中实现双重正确预测
Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding
AI总结：
针对人机协作中低延迟需求，提出基于双重正确预测的剪枝策略，在保持证据定位的同时提升预测准确性，在自我中心视频数据集上实现最高精度与双重正确性。
摘要：
视觉语言模型（VLM）在自我中心视觉理解中的快速崛起使得人机协作（HRC）任务中的低延迟推理变得日益关键。为VLM开发的权重剪枝技术可缩小模型尺寸和计算量，从而满足机载处理和实时交互机器人系统的效率需求。此外，安全的人机交互要求剪枝策略保留双重正确预测：输出必须既准确又有证据基础，以降低风险并确保用户信任。本文通过双重正确预测的视角对VLM剪枝进行了新研究。我们的实验令人惊讶地发现，现有剪枝方法往往保留了正确的证据定位，但破坏了正确预测。为解决这一问题，我们提出了一种基于理由的剪枝策略，更好地将证据与决策对齐。在自我中心视频数据集上的基准测试结果表明，我们的方法不仅实现了最高的预测准确性，而且在实现双重正确预测方面优于现有方法。我们旨在激发对高效可靠VLM的研究，确保以准确性驱动的进步与负责任的人机交互和具身智能所需的透明度、可审计性和安全性保持一致。
英文摘要：
The rapid rise of Vision-Language Models (VLMs) in egocentric visual understanding has made low-latency inference in human-robot collaborative (HRC) tasks increasingly critical. Weight pruning techniques developed for VLMs to shrink model size and computation can be readily applied to satisfy the efficiency demands of on-board processing and real-time interactive robotics. Moreover, safe human-robot interaction demands pruning strategies that preserve doubly-correct predictions; outputs must be both accurate and evidentially grounded to mitigate risks and ensure user trust. In this paper, we present a new study of VLM pruning through the lens of doubly-correct prediction. Our experiments surprisingly show that existing pruning methods often preserve the right evidence localization but undermine correct prediction. To address this, we propose a rationale-informed pruning strategy that better aligns evidence with decisions. Benchmark results on egocentric video datasets demonstrate that our method not only achieves the highest prediction accuracy but also outperforms existing approaches in attaining doubly-correct predictions. We aim to stimulate research on efficient and reliable VLMs, ensuring accuracy-driven advances align with the transparency, auditability, and safety required for responsible human-robot interaction and embodied intelligence.
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。
