Image Classification

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Overview

Image classification studies how to assign one or more labels to an image, supporting applications from species identification and medical diagnosis to document processing and remote sensing. Common approaches include convolutional neural networks, vision transformers, and pretrained vision-language models, trained with supervised, self-supervised, transfer, or few-shot learning. Researchers also develop data augmentation, hierarchical prediction, uncertainty estimation, and efficient models for settings such as ImageNet, corruption benchmarks, and specialized domain datasets. A central challenge is that strong accuracy on standard test sets may not transfer under distribution shifts or reveal reliance on spurious cues, while improving robustness, calibration, and efficiency can involve trade-offs.

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391 papers29 papers in the last four weeks, down 34% on the four weeks before. 0.2% of all new papers.

Nov 20, 2022

MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration

Open-set recognition refers to the problem in which classes that were not seen during training appear at inference time. This requires the ability to identify instances of novel classes while maintaining discriminative capability for closed-set classification. OpenMax was the first deep neural network-based approach to address open-set recognition by calibrating the predictive scores of a standard closed-set classification network. In this paper we present MetaMax, a more effective post-processing technique that improves upon contemporary methods by directly modeling class activation vectors. MetaMax removes the need for computing class mean activation vectors (MAVs) and distances between a query image and a class MAV as required in OpenMax. Experimental results show that MetaMax outperforms OpenMax and is comparable in performance to other state-of-the-art approaches.

Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi

The University of Texas at Arlington Arlington, TX, USA

Probability CalibrationOpen-Set RecognitionModel CalibrationClassificationImage Classification
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