Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)

• URL: http://arxiv.org/abs/2409.20277v1
• Date: Mon, 30 Sep 2024 13:28:14 GMT
• Title: Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)
• Authors: Mingxu Feng, Dian Chao, Peng Zheng, Yang Yang,
• Abstract summary: The challenge required identifying whether a test sample belonged to the semantic classes of a classifier's training set. We proposed a hybrid approach, experimenting with the fusion of various post-hoc OOD detection techniques and different Test-Time Augmentation strategies. Our best-performing method combined Test-Time Augmentation with the post-hoc OOD techniques, achieving a strong balance between AUROC and FPR95 scores.
• Score: 6.998958192483059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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