Timezone: »
Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object detectors in the wild. Although distance-based OOD detection methods have demonstrated promise in image classification, they remain largely unexplored in object-level OOD detection. This paper bridges the gap by proposing a distance-based framework for detecting OOD objects, which relies on the model-agnostic representation space and provides strong generality across different neural architectures. Our proposed framework SIREN contributes two novel components: (1) a representation learning component that uses a trainable loss function to shape the representations into a mixture of von Mises-Fisher (vMF) distributions on the unit hypersphere, and (2) a test-time OOD detection score leveraging the learned vMF distributions in a parametric or non-parametric way. SIREN achieves competitive performance on both the recent detection transformers and CNN-based models, improving the AUROC by a large margin compared to the previous best method. Code is publicly available at https://github.com/deeplearning-wisc/siren.
Author Information
Xuefeng Du (UW-Madison)
Gabriel Gozum (Department of Computer Science, University of Wisconsin - Madison)
Yifei Ming (University of Wisconsin-Madison)
I'm a Ph.D. student at the University of Wisconsin-Madison. I’m broadly interested in trustworthy machine learning and representation learning. Research topics that I am currently focusing on include: out-of-distribution detection, domain generalization, supervised and self-supervised (multi-modal) representation learning. My prior research involves designing efficient algorithms and promoting fundamental understandings to enable reliable open-world learning. (e.g., impact of spurious correlation, sample efficiency, and multi-modality).
Yixuan Li (University of Wisconsin-Madison)
More from the Same Authors
-
2022 Poster: SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning »
Haobo Wang · Mingxuan Xia · Yixuan Li · Yuren Mao · Lei Feng · Gang Chen · Junbo Zhao -
2022 : Domain Generalization with Nuclear Norm Regularization »
Zhenmei Shi · Yifei Ming · Ying Fan · Frederic Sala · Yingyu Liang -
2023 Poster: Dream the Impossible: Outlier Imagination with Diffusion Models »
Xuefeng Du · Yiyou Sun · Jerry Zhu · Yixuan Li -
2023 Poster: A Graph-Theoretic Framework for Understanding Open-World Representation Learning »
Yiyou Sun · Zhenmei Shi · Yixuan Li -
2023 Poster: Learning to Augment Distributions for Out-of-distribution Detection »
Qizhou Wang · Zhen Fang · Yonggang Zhang · Feng Liu · Yixuan Li · Bo Han -
2022 Workshop: Robustness in Sequence Modeling »
Nathan Ng · Haoran Zhang · Vinith Suriyakumar · Chantal Shaib · Kyunghyun Cho · Yixuan Li · Alice Oh · Marzyeh Ghassemi -
2022 Poster: Delving into Out-of-Distribution Detection with Vision-Language Representations »
Yifei Ming · Ziyang Cai · Jiuxiang Gu · Yiyou Sun · Wei Li · Yixuan Li -
2022 Poster: Is Out-of-Distribution Detection Learnable? »
Zhen Fang · Yixuan Li · Jie Lu · Jiahua Dong · Bo Han · Feng Liu -
2022 Poster: OpenOOD: Benchmarking Generalized Out-of-Distribution Detection »
Jingkang Yang · Pengyun Wang · Dejian Zou · Zitang Zhou · Kunyuan Ding · WENXUAN PENG · Haoqi Wang · Guangyao Chen · Bo Li · Yiyou Sun · Xuefeng Du · Kaiyang Zhou · Wayne Zhang · Dan Hendrycks · Yixuan Li · Ziwei Liu -
2021 : Uncovering the Deep Unknowns of ImageNet Model: Challenges and Opportunties »
Yixuan Li -
2021 Poster: On the Importance of Gradients for Detecting Distributional Shifts in the Wild »
Rui Huang · Andrew Geng · Yixuan Li -
2021 Poster: Can multi-label classification networks know what they don’t know? »
Haoran Wang · Weitang Liu · Alex Bocchieri · Yixuan Li -
2021 Poster: ReAct: Out-of-distribution Detection With Rectified Activations »
Yiyou Sun · Chuan Guo · Yixuan Li -
2020 Poster: Energy-based Out-of-distribution Detection »
Weitang Liu · Xiaoyun Wang · John Owens · Yixuan Li