This report provides a detailed description of the method we explored and proposed in the OSR Challenge at the OOD-CV Workshop during ECCV 2024. The challenge required identifying whether a test sample belonged to the semantic classes of a classifier's training set, a task known as open-set recognition (OSR). Using the Semantic Shift Benchmark (SSB) for evaluation, we focused on ImageNet1k as the in-distribution (ID) dataset and a subset of ImageNet21k as the out-of-distribution (OOD) dataset.To address this, we proposed a hybrid approach, experimenting with the fusion of various post-hoc OOD detection techniques and different Test-Time Augmentation (TTA) strategies. Additionally, we evaluated the impact of several base models on the final performance. Our best-performing method combined Test-Time Augmentation with the post-hoc OOD techniques, achieving a strong balance between AUROC and FPR95 scores. Our approach resulted in AUROC: 79.77 (ranked 5th) and FPR95: 61.44 (ranked 2nd), securing second place in the overall competition.

本研究针对开放集识别（OSR）问题，提出了一种新的混合方法，通过融合多种后处理的OOD检测技术和测试时数据增强策略，解决了测试样本是否属于分类器训练集语义类别的识别任务。我们的最佳方案在AUROC和FPR95指标上取得了优异的平衡，最终在比赛中获得第二名，展示了我们方法的有效性和潜在影响。

2024年OOD-CV研讨会SSB挑战赛解决方案（开放集识别 track）