Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)
<|reference_start|>Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track): 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.<|reference_end|>
@article{feng2024solution,
    title={Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition
  Track)},
    author={Mingxu Feng, Dian Chao, Peng Zheng, Yang Yang},
    journal={arXiv preprint arXiv:2409.20277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20277},
    primaryClass={cs.CV cs.LG}
}