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Found 9 papers, 4 papers with code
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
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
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.
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples.
Ranked #12 on
Out-of-Distribution Detection
on ImageNet-1k vs Places
(AUROC metric)
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
Rail-5k: a Real-World Dataset for Rail Surface Defects Detection
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.
Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization
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
In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
