The long-tailed distribution datasets poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balacing strategies or transfer learing from head- to tail-classes or use two-stages learning strategy to re-train the classifier. However, the existing methods are difficult to solve the low quality problem when images are obtained by SAR. To address this problem, we establish a novel three-stages training strategy, which has excellent results for processing SAR image datasets with long-tailed distribution. Specifically, we divide training procedure into three stages. The first stage is to use all kinds of images for rough-training, so as to get the rough-training model with rich content. The second stage is to make the rough model learn the feature expression by using the residual dataset with the class 0 removed. The third stage is to fine tune the model using class-balanced datasets with all 10 classes (including the overall model fine tuning and classifier re-optimization). Through this new training strategy, we only use the information of SAR image dataset and the network model with very small parameters to achieve the top 1 accuracy of 22.34 in development phase.
Deep learning has achieved remarkable progress for visual recognition on large-scale balanced datasets but still performs poorly on real-world long-tailed data. Previous methods often adopt class re-balanced training strategies to effectively alleviate the imbalance issue, but might be a risk of over-fitting tail classes. The recent decoupling method overcomes over-fitting issues by using a multi-stage training scheme, yet, it is still incapable of capturing tail class information in the feature learning stage. In this paper, we show that soft label can serve as a powerful solution to incorpor…
Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more prevalent in the industry because they have a simple and fast pipeline that is easy to deploy. However, in the long-tailed scenario, this line of work has not been explored so far. In this paper, we investigate whether one-stage detectors can perform well in this case. We discover the primary obstacle that prevents one-stage detectors from achieving excellent performance is: categories suffer from different …
National University of Singapore（新加坡国立大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；ByteDance（字节跳动）
Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution. However, practical test class distributions often violate this assumption (e.g., being either long-tailed or even inversely long-tailed), which may lead existing methods to fail in real applications. In this paper, we study a more practical yet challenging task, called test-agnostic long-tailed recognition, where the training class distribution is long-tailed while the test class distribution is agnostic an…
One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i.e., either sacrifice the head's accuracy for better tail classification or elevate the head's accuracy even higher but ignore the tail. Existing algorithms bypass such trade-off by a multi-stage training process: pre-training on imbalanced set and fine-tuning on balanced set. Though achieving promising performance, not only are they sensitive to the generalizability of the pre-trained model, but also not easily integrated into other computer vision tasks like detection and segmentation, where pre-…
UC, San Diego（加州大学圣迭戈分校）；Wormpex AI Research（Wormpex AI 研究院）
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effective for this problem, it is known to over-fit to few-shot classes. It is hypothesized that this is due to the repeated sampling of examples and can be addressed by feature space augmentation. A new feature augmentation strategy, EMANATE, based on back-tracking of features across epochs during training, is proposed. It is shown that, unlike class-balanced sampling, this is an adversarial augmentation strategy. A new …
Robert Bosch Center for Data Science and Artificial Intelligence（罗伯特·博世数据科学与人工智能中心）；Indian Institute of Technology Madras（马德拉斯印度理工学院）
Synthesizing high-quality, realistic images from text-descriptions is a challenging task, and current methods synthesize images from text in a multi-stage manner, typically by first generating a rough initial image and then refining image details at subsequent stages. However, existing methods that follow this paradigm suffer from three important limitations. Firstly, they synthesize initial images without attempting to separate image attributes at a word-level. As a result, object attributes of initial images (that provide a basis for subsequent refinement) are inherently entangled and ambigu…
Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to improve performance. But there is still the vital issue of miscalibration. To address it, we design two methods to improve calibration and performance in such scenarios. Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier lea…
Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
East China Normal University（华东师范大学）；Kargobot of DiDi（滴滴出行Kargobot）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua Berkeley Shenzhen Institute（清华大学深圳国际研究生院（清华-伯克利深圳学院））；Toronto Metropolitan University（多伦多都会大学）
Real-world imagery is often characterized by a significant imbalance of the number of images per class, leading to long-tailed distributions. An effective and simple approach to long-tailed visual recognition is to learn feature representations and a classifier separately, with instance and class-balanced sampling, respectively. In this work, we introduce a new framework, by making the key observation that a feature representation learned with instance sampling is far from optimal in a long-tailed setting. Our main contribution is a new training method, referred to as Class-Balanced Distillati…
Tongji University（同济大学）；Sensetime Research（商汤科技研究院）；University of Sydney（悉尼大学）；Shanghai AI Laboratory（上海人工智能实验室）
Long-tail distribution is widely spread in real-world applications. Due to the extremely small ratio of instances, tail categories often show inferior accuracy. In this paper, we find such performance bottleneck is mainly caused by the imbalanced gradients, which can be categorized into two parts: (1) positive part, deriving from the samples of the same category, and (2) negative part, contributed by other categories. Based on comprehensive experiments, it is also observed that the gradient ratio of accumulated positives to negatives is a good indicator to measure how balanced a category is tr…
Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resampling is typically introduced as a simple but effective approach. However, we observe that long-tailed detection differs from classification since multiple classes may be present in one image. As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level. We address object-level resampling by introducing an object-centric memory replay strategy based on dynamic, ep…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, which deteriorates the performance of the recognition model. In this paper, we propose a novel long-tailed recognition method to balance the latent feature space. First, we introduce a MixUp-based data augmentation technique to reduce the bias of the long-tailed data. Furthermore, we propose a new supervised contrastive learning method, named Supervised contrastive learning on Mixed Classes (SMC), for blended images. S…
Xiaohe Healthcare, ByteDance（字节跳动小荷健康）；The University of Hong Kong（香港大学）
Medical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image datasets are usually highly-imbalanced. To address this problem, previous works utilized class samples as prior for re-weighting or re-sampling but the feature representation is usually still not discriminative enough. In this paper, we adopt the contrastive learning to tackle the long-tailed medical imbalance problem. Specifically, we first propose the category prototype and adversarial proto-instance to generate repr…
Nankai University（南开大学）；University of Florence（佛罗伦萨大学）；Tencent Youtu Lab（腾讯优图实验室）
In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distribution for each new task, which ignores the prevalence of long-tailed distributions in the real world. In this work we propose two long-tailed CIL scenarios, which we term ordered and shuffled LT-CIL. Ordered LT-CIL considers the scenario where we learn from head classes collected with more samples than tail classes which have few. Shuffled LT-CIL, on the other hand, assumes a completely random long-tailed distributio…
In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training. However, due to the limited training images for tail classes, the diversity of tail class images is still restricted, which results in poor feature representations. In this work, we hypothesize that common latent features among the head and tail classes can be used to give better feature representation. Motivated by this, we i…
Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
Key Lab. of Intelligent Information Processing, Institute of Computing Technology, CAS（智能信息处理重点实验室，计算技术研究所，中国科学院）；School of Computer Science and Tech., University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Institute of Information Engineering, CAS（信息工程研究所，中国科学院）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Deep neural networks still struggle on long-tailed image datasets, and one of the reasons is that the imbalance of training data across categories leads to the imbalance of trained model parameters. Motivated by the empirical findings that trained classifiers yield larger weight norms in head classes, we propose to reformulate the recognition probabilities through included angles without re-balancing the classifier weights. Specifically, we calculate the angles between the data feature and the class-wise classifier weights to obtain angle-based prediction results. Inspired by the performance i…
Flexible Sampling for Long-tailed Skin Lesion Classification
Monash-Airdoc Research（莫纳什-艾尔多克研究）；Monash University（莫纳什大学）；Monash Medical AI Group（莫纳什医学人工智能小组）；Faculty of Information Technology（信息科技学院）；Harbin Engineering University（哈尔滨工程大学）
UC, San Diego（加州大学圣迭戈分校）；Wormpex AI Research（Wormpex AI 研究院）
Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a more balanced training loss and to improve tail classes data variations with synthesized samples, we resort to leverage readily available unlabeled data to boost recognition accuracy. The idea leads to a new recognition setting, namely semi-supervised long-tailed recognition. We argue this setting better resembles the real-world data collection and annotation process and hence can help close the gap to real-world scenari…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu Research（百度研究院）；National Engineering Laboratory for Deep Learning Technology and Application（深度学习技术及应用国家工程实验室）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人创新中心）
Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a t…
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；University of British Columbia（不列颠哥伦比亚大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Harbin Institute of Technology（哈尔滨工业大学）；National University of Singapore（新加坡国立大学）；Peng Cheng Laboratory（鹏城实验室）
For long-tailed classification, most works often pretrain a big model on a large-scale dataset, and then fine-tune the whole model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization ability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning method for long-tailed classification. LPT introduces several trainable prompts into a frozen pretr…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Data augmentation for minority classes is an effective strategy for long-tailed recognition, thus developing a large number of methods. Although these methods all ensure the balance in sample quantity, the quality of the augmented samples is not always satisfactory for recognition, being prone to such problems as over-fitting, lack of diversity, semantic drift, etc. For these issues, we propose the Class-aware Universum Inspired Re-balance Learning(CaUIRL) for long-tailed recognition, which endows the Universum with class-aware ability to re-balance individual minority classes from both sample…
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will h…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Inner Mongolia Key Laboratory of Molecular Biology on Featured Plants（内蒙古特色植物分子生物学重点实验室）
Mixup is a popular data augmentation method, with many variants subsequently proposed. These methods mainly create new examples via convex combination of random data pairs and their corresponding one-hot labels. However, most of them adhere to a random sampling and mixing strategy, without considering the frequency of label occurrence in the mixing process. When applying mixup to long-tailed data, a label suppression issue arises, where the frequency of label occurrence for each class is imbalanced and most of the new examples will be completely or partially assigned with head labels. The supp…
The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Huawei（华为）；Tsinghua University（清华大学）；SenseTime-CUHK Joint Lab（商汤科技-香港中文大学联合实验室）；Centre of Perceptual and Interactive Intelligence（感知与交互智能中心）；Shanghai AI Laboratory（上海人工智能实验室）
Adversarial robustness has attracted extensive studies recently by revealing the vulnerability and intrinsic characteristics of deep networks. However, existing works on adversarial robustness mainly focus on balanced datasets, while real-world data usually exhibits a long-tailed distribution. To push adversarial robustness towards more realistic scenarios, in this work we investigate the adversarial vulnerability as well as defense under long-tailed distributions. In particular, we first reveal the negative impacts induced by imbalanced data on both recognition performance and adversarial rob…
The Chinese University of Hong Kong（香港中文大学）；Microsoft Research Asia（微软亚洲研究院）
In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebalance. Then a simple and yet effective region rebalance scheme is derived based on our analysis. In our solution, pixel features belonging to the same class are grouped into region features, and a rebalanced region classifier is applied via an auxiliary region rebalance branch during training. To verify the flexibility and effectiveness of our method, we apply the region rebalance module into various semantic segmentat…
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Inceptio Tech.（嬴彻科技）
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes. This imbalance degrades the performance of typical supervised learning algorithms designed for balanced training sets. In this paper, we address this issue by augmenting minority classes with a recently proposed implicit semantic data augmentation (ISDA) algorithm, which produces diversified augmented samples by translating deep features along many semantically meaningful directions. Importantly, given that ISDA es…
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室（深圳））；Shenzhen University（深圳大学）；Hong Kong Baptist University（香港浸会大学）；Xiamen University（厦门大学）
The Chinese University of Hong Kong（香港中文大学）；MEGVII Technology（旷视科技）
Deep neural networks perform poorly on heavily class-imbalanced datasets. Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition. Based on the mathematical analysis and simulation results, we claim that supervised contrastive learning suffers a dual class-imbalance problem at both the original batch and Siamese batch levels, which is more serious than long-tailed classification learning. In this paper, at the original batch level, we introduce a class-balanced supervised contrastive loss to assign adap…
The Ohio State University（俄亥俄州立大学）；Google Research（谷歌研究院）；University of Southern California（南加州大学）
Many objects do not appear frequently enough in complex scenes (e.g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e.g., in product images). Yet, these object-centric images are not effectively leveraged for improving object detection in scene-centric images. In this paper, we propose Mosaic of Object-centric images as Scene-centric images (MosaicOS), a simple and novel framework that is surprisingly effective at tackling the challenges of long-tailed object detection. Keys to our approach are three-fold: (i) pseudo s…
An Enhanced Classification Method Based on Adaptive Multi-Scale Fusion for Long-tailed Multispectral Point Clouds
School of Electronics and Information Engineering（电子信息工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；the Department of Telecommunications and Information Processing（电信与信息处理系）
In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. Naive training produces models that are biased toward common classes in terms of higher accuracy. The key to addressing LTR is to balance various aspects including data distribution, training losses, and gradients in learning. We explore an orthogonal direction, weight balancing, motivated by the empirical observation that the naively trained classifier has "artificially" larger weights in norm for common classes (because there exists abundant data to…
University of Wollongong（卧龙岗大学）；University of Bristol（布里斯托尔大学）；Nanjing University of Science and Technology（南京理工大学）；Northwestern Polytechnical University（西北工业大学）
Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image repre…
Group-wise Supervision with Focal-Dice Loss for Long-Tailed Indoor Semantic Occupancy Prediction
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Guangdong Key Laboratory of Intelligent Information Processing（广东省智能信息处理重点实验室）
University of Science and Technology of China（科学技术大学）；Hefei University of Technology（合肥工业大学）；IAI, Hefei Comprehensive National Science Center（IAI合肥国家科学中心）
Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification
Research Center for Space Computing System, Zhejiang Lab（空间计算系统研究中心，浙江实验室）；School of Cyber Science and Engineering, Zhengzhou University（网络安全工程学院，郑州大学）；School of Computer Science and Technology, Xi'an Jiaotong University（计算机科学与技术学院，西安交通大学）
Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier
Anhui University（安徽大学）；Hefei University of Technology（合肥工业大学）；Pennsylvania State University（宾夕法尼亚州立大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
