Search Results for author: Shaozuo Yu

Found 9 papers, 4 papers with code

MOODv2: Masked Image Modeling for Out-of-Distribution Detection

no code implementations5 Jan 2024 Jingyao Li, Pengguang Chen, Shaozuo Yu, Shu Liu, Jiaya Jia

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

BAL: Balancing Diversity and Novelty for Active Learning

1 code implementation26 Dec 2023 Jingyao Li, Pengguang Chen, Shaozuo Yu, Shu Liu, Jiaya Jia

Experimental results demonstrate that, when labeling 80% of the samples, the performance of the current SOTA method declines by 0. 74%, whereas our proposed BAL achieves performance comparable to the full dataset.

Active Learning Self-Supervised Learning

Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need

1 code implementation CVPR 2023 Jingyao Li, Pengguang Chen, Shaozuo Yu, Zexin He, Shu Liu, Jiaya Jia

The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples.

Out-of-Distribution Detection

OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images

no code implementations29 Nov 2021 Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski

One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.

3D Pose Estimation Benchmarking +5

Rail-5k: a Real-World Dataset for Rail Surface Defects Detection

no code implementations28 Jun 2021 Zihao Zhang, Shaozuo Yu, Siwei Yang, Yu Zhou, Bingchen Zhao

This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.

Benchmarking

Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization

1 code implementation6 Jun 2021 Siwei Yang, Shaozuo Yu, Bingchen Zhao, Yin Wang

Visual pattern recognition over agricultural areas is an important application of aerial image processing.

Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning

no code implementations5 Jun 2021 Yilin Wang, Shaozuo Yu, Xiaokang Yang, Wei Shen

In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy.

Classification

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