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BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
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TLDR
A unified Bilateral-Branch Network (BBN) is proposed to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately.Abstract:
Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these re-balancing methods achieving satisfactory recognition accuracy owe to that they could significantly promote the classifier learning of deep networks. However, at the same time, they will unexpectedly damage the representative ability of the learned deep features to some extent. Therefore, we propose a unified Bilateral-Branch Network (BBN) to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately. In particular, our BBN model is further equipped with a novel cumulative learning strategy, which is designed to first learn the universal patterns and then pay attention to the tail data gradually. Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods. Furthermore, validation experiments can demonstrate both our preliminary discovery and effectiveness of tailored designs in BBN for long-tailed problems. Our method won the first place in the iNaturalist 2019 large scale species classification competition, and our code is open-source and available at this https URL.read more
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Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang,Zhi Xu +1 more
TL;DR: It is demonstrated, theoretically and empirically, that class-imbalanced learning can significantly benefit in both semi- supervised and self-supervised manners and the need to rethink the usage of imbalanced labels in realistic long-tailed tasks is highlighted.
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Balanced Meta-Softmax for Long-Tailed Visual Recognition
TL;DR: Balanced Softmax is presented, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing, and it is demonstrated that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
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Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
Xi Ouyang,Jiayu Huo,Liming Xia,Fei Shan,Jun Liu,Zhanhao Mo,Fuhua Yan,Zhongxiang Ding,Qi Yang,Bin Song,Feng Shi,Huan Yuan,Ying Wei,Xiaohuan Cao,Yaozong Gao,Dijia Wu,Qian Wang,Dinggang Shen +17 more
TL;DR: A dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) with a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.
Proceedings ArticleDOI
Long- Tailed Recognition via Weight Balancing
TL;DR: An orthogonal direction, weight balancing, is explored by the empirical observation that the naively trained classifier has “artificially” larger weights in norm for common classes (because there exists abundant data to train them, unlike the rare classes) and achieves the state-of-the-art accuracy on five standard benchmarks.
Proceedings ArticleDOI
Balanced Contrastive Learning for Long-Tailed Visual Recognition
TL;DR: To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, a novel loss for balanced contrastive learning (BCL) is proposed that satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Dissertation
Learning Multiple Layers of Features from Tiny Images
TL;DR: In this paper, the authors describe how to train a multi-layer generative model of natural images, using a dataset of millions of tiny colour images, described in the next section.
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Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.
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Focal Loss for Dense Object Detection
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.