Open-world learning (OWL) requires models that can adapt to evolving environments while reliably detecting out-of-distribution (OOD) inputs. Existing approaches, such as SCONE, achieve robustness to covariate and semantic shifts but assume static environments, leading to degraded performance in dynamic domains. In this paper, we propose Temp-SCONE, a temporally consistent extension of SCONE designed to handle temporal shifts in dynamic environments. Temp-SCONE introduces a confidence-driven regularization loss based on Average Thresholded Confidence (ATC), penalizing instability in predictions across time steps while preserving SCONE's energy-margin separation. Experiments on dynamic datasets demonstrate that Temp-SCONE significantly improves robustness under temporal drift, yielding higher corrupted-data accuracy and more reliable OOD detection compared to SCONE. On distinct datasets without temporal continuity, Temp-SCONE maintains comparable performance, highlighting the importance and limitations of temporal regularization. Our theoretical insights on temporal stability and generalization error further establish Temp-SCONE as a step toward reliable OWL in evolving dynamic environments.
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Institute for Foundations of Data Science, University of Wisconsin, Madison（数据科学基础研究院，威斯康星大学麦迪逊分校）；Department of Computer Sciences, University of Wisconsin, Madison（计算机科学系，威斯康星大学麦迪逊分校）；Department of Electrical and Computer Engineering, University of Wisconsin, Madison（电气与计算机工程系，威斯康星大学麦迪逊分校）
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Faculty of Science, University of Alberta（阿尔伯塔大学理学院）；Interdisciplinary Lab for Mathematical Ecology & Epidemiology (ILMEE), University of Alberta（阿尔伯塔大学数学生态与流行病学跨学科实验室）；Department of Mathematical and Statistical Sciences, University of Alberta（阿尔伯塔大学数学与统计科学系）
本文讨论了情感分类的问题。该任务包括预测最能描述短视频剪辑中包含的情绪的情绪标签（从一组可能的标签中选取）。建立在一个标准的框架-躺在描述视频的音频和视觉特征使用的监督分类器来推断标签-本文研究了几个新的方向。首先，提出了基于二维和三维卷积神经网络的改进人脸描述子。其次，本文探讨了几种融合方法，时间和多模态，包括一种新的分层方法相结合的功能和分数。此外，我们仔细审查了流水线的不同阶段，并设计了一个适应任务的CNN架构;这一点很重要，因为与问题的难度相比，训练集的大小很小，使得泛化困难。该模型在2017年的Emotion in the Wild挑战赛中排名第四，准确率为58.8%。
