Long-Tail Buzz - 2022
Maintained by Rahul Vigneswaran
Long-Tail Buzz displays the most discussed long-tail papers at top AI conferences (2022).
Retrieval Augmented Classification for Long-Tail Visual Recognition
Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. [Expand]
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99Long-Tailed Recognition via Weight Balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong
In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. [Expand]
7 2 16 84On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Yuzhe Yang, Hao Wang, Dina Katabi
Real-world data often exhibit imbalanced label distributions. [Expand]
1 0 17 69Equalized Focal Loss for Dense Long-Tailed Object Detection
Bo Li, Yongqiang Yao, Jingru Tan, Gang Zhang, Fengwei Yu, Jianwei Lu, Ye Luo
Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. [Expand]
11Targeted Supervised Contrastive Learning for Long-Tailed Recognition
Tianhong Li, Peng Cao, Yuan Yuan, Lijie Fan, Yuzhe Yang, Rogerio S. Feris, Piotr Indyk, Dina Katabi
Real-world data often exhibits long tail distributions with heavy class imbalance, where the majority classes can dominate the training process and alter the decision boundaries of the minority classes. [Expand]
10Relieving Long-Tailed Instance Segmentation via Pairwise Class Balance
Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei, Xiangyu Zhang, Jian Sun
Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. [Expand]
3 2 0 2Trustworthy Long-Tailed Classification
Bolian Li, Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang
Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and accordingly unpromising performance especially on tail classes. [Expand]
4Balanced Contrastive Learning for Long-Tailed Visual Recognition
Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, Yu-Gang Jiang
Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. [Expand]
4The Majority Can Help the Minority: Context-Rich Minority Oversampling for Long-Tailed Classification
Seulki Park, Youngkyu Hong, Byeongho Heo, Sangdoo Yun, Jin Young Choi
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. [Expand]
3VL-LTR: Learning Class-Wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
Changyao Tian, Wenhai Wang, Xizhou Zhu, Jifeng Dai, Yu Qiao
Recently, computer vision foundation models such as CLIP and ALI-GN, have shown impressive generalization capabilities on various downstream tasks. [Expand]
3Nested Collaborative Learning for Long-Tailed Visual Recognition
Jun Li, Zichang Tan, Jun Wan, Zhen Lei, Guodong Guo
The networks trained on the long-tailed dataset vary remarkably, despite the same training settings, which shows the great uncertainty in long-tailed learning. [Expand]
1 1 1 3Constructing Balance from Imbalance for Long-Tailed Image Recognition
Yue Xu, Yong-Lu Li, Jiefeng Li, Cewu Lu
Long-tailed image recognition presents massive challenges to deep learning systems since the imbalance between majority (head) classes and minority (tail) classes severely skews the data-driven deep neural networks. [Expand]
2 0 0 0Long-Tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Mengke Li, Yiu-ming Cheung, Yang Lu
Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. [Expand]
2RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition
Jun Chen, Aniket Agarwal, Sherif Abdelkarim, Deyao Zhu, Mohamed Elhoseiny
The visual relationship recognition (VRR) task aims at understanding the pairwise visual relationships between interacting objects in an image. [Expand]
1 1 4Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition
Bo Liu, Haoxiang Li, Hao Kang, Gang Hua, Nuno Vasconcelos
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. [Expand]
1Invariant Feature Learning for Generalized Long-Tailed Classification
Kaihua Tang, Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, Hanwang Zhang
Existing long-tailed classification (LT) methods only focus on tackling the class-wise imbalance that head classes have more samples than tail classes, but overlook the attribute-wise imbalance. [Expand]
1Identifying Hard Noise in Long-Tailed Sample Distribution
Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang
Conventional de-noising methods rely on the assumption that the noisy samples are independent and identically distributed, so the resultant classifier, though disturbed by noise, can still easily identify the noises as outliers. [Expand]
1Long-Tailed Instance Segmentation Using Gumbel Optimized Loss
Konstantinos Panagiotis Alexandridis, Jiankang Deng, Anh Nguyen, Shan Luo
Major advancements have been made in the field of object detection and segmentation recently. [Expand]
1Long-Tail Recognition via Compositional Knowledge Transfer
Sarah Parisot, Pedro M. Esperança, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer. [Expand]
1Tackling Long-Tailed Category Distribution under Domain Shifts
Xiao Gu, Yao Guo, Zeju Li, Jianing Qiu, Qi Dou, Yuxuan Liu, Benny Lo, Guang-Zhong Yang
Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2) the test data is drawn from different conditional distributions P(X|Y). [Expand]
1C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection
Tong Wang, Yousong Zhu, Yingying Chen, Chaoyang Zhao, Bin Yu, Jinqiao Wang, Ming Tang
Long-tail object detection suffers from poor performance on tail categories. [Expand]
1Long-Tailed Class Incremental Learning
Xialei Liu, Yu-Song Hu, Xu-Sheng Cao, Andrew D. Bagdanov, Ke Li, Ming-Ming Cheng
In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. [Expand]
0 0 1Adaptive Hierarchical Representation Learning for Long-Tailed Object Detection
Banghuai Li
General object detectors are always evaluated on hand-designed datasets, e.g., MS COCO and Pascal VOC, which tend to maintain balanced data distribution over different classes. [Expand]
Learning with Free Object Segments for Long-Tailed Instance Segmentation
Cheng Zhang, Tai-Yu Pan, Tianle Chen, Jike Zhong, Wenjin Fu, Wei-Lun Chao
One fundamental challenge in building an instance segmentation model for a large number of classes in complex scenes is the lack of training examples, especially for rare objects. [Expand]
Improving GANs for Long-Tailed Data through Group Spectral Regularization
Harsh Rangwani, Naman Jaswani, Tejan Karmali, Varun Jampani, R. Venkatesh Babu
Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples. [Expand]
Improving the Intra-Class Long-Tail in 3D Detection via Rare Example Mining
Chiyu Max Jiang, Mahyar Najibi, Charles R. Qi, Yin Zhou, Dragomir Anguelov
Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. [Expand]
Long-Tail Detection with Effective Class-Margins
Jang Hyun Cho, Philipp Krähenbühl
Large-scale object detection and instance segmentation faces a severe data imbalance. [Expand]
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification
Yan Hong, Jianfu Zhang, Zhongyi Sun, Ke Yan
Imbalanced datasets with long-tailed distribution widely exist in practice, posing great challenges for deep networks on how to handle the biased predictions between head (majority, frequent) classes and tail (minority, rare) classes. [Expand]
Show TweetsTowards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning
Hualiang Wang, Siming Fu, Xiaoxuan He, Hangxiang Fang, Zuozhu Liu, Haoji Hu
Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. [Expand]