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arXiv · 2209.04861

Inverse Image Frequency for Long-tailed Image Recognition

Konstantinos Panagiotis Alexandridis·Shan Luo·Anh Nguyen·Jiankang Deng·Stefanos Zafeiriou
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Abstract

The long-tailed distribution is a common phenomenon in the real world. Extracted large scale image datasets inevitably demonstrate the long-tailed property and models trained with imbalanced data can obtain high performance for the over-represented categories, but struggle for the under-represented categories, leading to biased predictions and performance degradation. To address this challenge, we propose a novel de-biasing method named Inverse Image Frequency (IIF). IIF is a multiplicative margin adjustment transformation of the logits in the classification layer of a convolutional neural network. Our method achieves stronger performance than similar works and it is especially useful for downstream tasks such as long-tailed instance segmentation as it produces fewer false positive detections. Our extensive experiments show that IIF surpasses the state of the art on many long-tailed benchmarks such as ImageNet-LT, CIFAR-LT, Places-LT and LVIS, reaching 55.8% top-1 accuracy with ResNet50 on ImageNet-LT and 26.2% segmentation AP with MaskRCNN on LVIS. Code available at https://github.com/kostas1515/iif

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Konstantinos Panagiotis Alexandridis, Shan Luo, Anh Nguyen, Jiankang Deng, Stefanos Zafeiriou. 2022-09-11. Inverse Image Frequency for Long-tailed Image Recognition. https://doi.org/10.1109/tip.2023.3321461

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