ProbRes：面向开放世界第一人称活动识别的概率跳跃扩散方法
ProbRes: Probabilistic Jump Diffusion for Open-World Egocentric Activity Recognition
AI总结：
针对开放世界第一人称活动识别的挑战，提出基于跳跃扩散的概率残差搜索框架ProbRes，结合常识先验与VLMs进行高效搜索，在多个基准上达到SOTA并建立了开放世界识别分类法。
摘要：
开放世界第一人称活动识别由于其无约束的特性而构成根本性挑战，要求模型从广阔且部分观测的搜索空间中推断未见活动。我们提出了ProbRes，一个基于跳跃扩散的概率残差搜索框架，通过平衡先验引导的探索与似然驱动的利用来高效地导航该空间。我们的方法整合了结构化常识先验以构建语义连贯的搜索空间，使用视觉语言模型（VLMs）自适应地细化预测，并采用随机搜索机制来定位高似然的活动标签，同时高效地最小化穷举枚举。我们在多个开放度级别（L0-L3）上系统评估了ProbRes，展示了其对日益增加的搜索空间复杂度的适应性。除了在基准数据集（GTEA Gaze、GTEA Gaze+、EPIC-Kitchens和Charades-Ego）上取得最先进的性能外，我们还为开放世界识别建立了清晰的分类法，界定了第一人称活动理解所需的挑战和方法论进展。我们的结果突出了结构化搜索策略的重要性，为可扩展且高效的开放世界活动识别铺平了道路。
英文摘要：
Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided exploration with likelihood-driven exploitation. Our approach integrates structured commonsense priors to construct a semantically coherent search space, adaptively refines predictions using Vision-Language Models (VLMs) and employs a stochastic search mechanism to locate high-likelihood activity labels while minimizing exhaustive enumeration efficiently. We systematically evaluate ProbRes across multiple openness levels (L0-L3), demonstrating its adaptability to increasing search space complexity. In addition to achieving state-of-the-art performance on benchmark datasets (GTEA Gaze, GTEA Gaze+, EPIC-Kitchens, and Charades-Ego), we establish a clear taxonomy for open-world recognition, delineating the challenges and methodological advancements necessary for egocentric activity understanding. Our results highlight the importance of structured search strategies, paving the way for scalable and efficient open-world activity recognition.
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