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Article
Computer Science, Artificial Intelligence
Zhihao Wang et al.
Summary: This article provides a comprehensive survey on recent advances of image super-resolution using deep learning approaches, categorizing existing studies into supervised, unsupervised, and domain-specific SR techniques, as well as covering benchmark datasets and evaluation metrics. Future directions and open issues in the field are also highlighted for further research.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
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Jianping Gou et al.
Summary: This paper provides a comprehensive survey of knowledge distillation, covering knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison, and applications. It also briefly reviews challenges in knowledge distillation and discusses future research directions.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
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Yoshua Bengio et al.
Summary: Research on artificial neural networks is motivated by the observation that human intelligence emerges from parallel networks of simple non-linear neurons, leading to the question of how these networks can learn complicated internal representations.
COMMUNICATIONS OF THE ACM
(2021)
Article
Computer Science, Artificial Intelligence
Yifan Zhang et al.
Summary: Online Active Learning aims to manage unlabeled datastream by selectively querying labels, but faces challenges such as limited query budget and class imbalance. Previous studies use asymmetric strategies and second-order optimization to address these challenges, while the proposed novel algorithm combines asymmetric losses and queries strategies and enhances efficiency through sketching technique. Promising results demonstrate the effectiveness and efficiency of the proposed methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yuhang Zang et al.
Summary: The Feature Augmentation and Sampling Adaptation (FASA) method addresses the data scarcity issue for rare classes in long-tailed instance segmentation. FASA is a fast, generic method that can be easily applied to standard or long-tailed segmentation frameworks, showing consistent performance gains.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Cheng Zhang et al.
Summary: This paper introduces a simple and novel framework called "MOSAICOS", which effectively addresses the challenges of long-tailed object detection. The key to this framework lies in pseudo scene-centric image construction, high-quality bounding box imputation, and multi-stage training. Experimental results show a significant relative improvement in average precision for rare object categories with MOSAICOS.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xing Zhang et al.
Summary: Label distributions in real-world are often long-tailed and imbalanced, leading to biased models towards dominant labels. VideoLT dataset is introduced as a step towards real-world video recognition, with the proposed FrameStack method addressing the issue by performing frame-level sampling for balancing class distributions in video recognition.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zizhao Zhang et al.
Summary: Training sample re-weighting is an effective approach for addressing data biases, but existing methods have limitations such as relying on unbiased reward data and requiring expensive second-order computation. This paper introduces a novel learning-based fast sample re-weighting (FSR) method that overcomes these limitations by learning from history and sharing features to reduce optimization costs. Experimental results show that the proposed FSR method achieves competitive performance in label noise robustness and long-tailed recognition while significantly improving training efficiency.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Tianhao Li et al.
Summary: This paper introduces a multi-stage training scheme using soft labels for long-tailed recognition, which effectively transfers knowledge from head to tail classes.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Seulki Park et al.
Summary: This paper proposes a balancing training method with a new loss function that improves the performance of imbalanced data learning. Experimental results demonstrate the effectiveness of the proposed method and its superiority over state-of-the-art cost-sensitive loss methods.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jiequan Cui et al.
Summary: This paper introduces Parametric Contrastive Learning (PaCo) to address long-tailed recognition, by introducing learnable class-wise centers to rebalance the data from an optimization perspective. Experiments show that PaCo can adaptively enhance the effectiveness of pushing samples of the same class closer, achieving a new state-of-the-art in long-tailed recognition tasks.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhuowan Li et al.
Summary: Neural symbolic methods demonstrate strong performance in synthetic images but struggle in real images, mainly due to the long-tail distribution of visual concepts and unequal importance of reasoning steps. The proposed CCO paradigm addresses these challenges by enabling models to capture underlying data characteristics and reason with hierarchical importance, significantly boosting their performance on real images and reducing the performance gap between symbolic and non-symbolic methods.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Bo Liu et al.
Summary: This paper addresses the problem of long-tailed recognition and proposes a transfer learning method based on exploiting overfitting to transfer geometric information from popular classes to low-shot classes for better generalization performance. A new classifier architecture and learning algorithm are introduced, with experiments showing superior performance compared to existing solutions.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Dvir Samuel et al.
Summary: Research shows that the feature extractor of deep networks is greatly affected by the bias introduced by unbalanced data learning. A new loss based on robustness theory is proposed to reduce representation bias towards head classes in the feature space and achieves new state-of-the-art results on various long-tail benchmark datasets.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Alakh Desai et al.
Summary: Researchers have explored an alternative approach to dealing with long-tailed problems by simplifying the model while improving its ability to handle long-tailed distributions for better performance. They introduced the Decoupled Training for Devil in the Tails (DT2) method, using Alternating Class-Balanced Sampling (ACBS) to train visual relationship models and achieved significant advantages in scene graph generation tasks.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhenzhen Weng et al.
Summary: This research aims to propose an unsupervised method to discover long-tail categories in instance segmentation by learning instance embeddings of masked regions. By leveraging self-supervised losses for learning mask embeddings, the model trained on COCO dataset is able to identify novel and more fine-grained objects than common categories. The model achieves competitive quantitative results on LVIS compared to supervised and partially supervised methods.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Tong Wu et al.
Summary: This study investigates adversarial vulnerability and defense mechanisms under long-tailed distributions, revealing the negative impacts of imbalanced data and proposing a new framework RoBal with two dedicated modules to enhance adversarial robustness. Experiment results show the superiority of the proposed approach over state-of-the-art defense methods, making it a significant step towards real-world robustness.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zongyong Deng et al.
Summary: In this paper, a progressive margin loss (PML) approach for unconstrained facial age classification is proposed, aiming to adaptively refine the age label pattern by enforcing margins that consider the in-between discrepancy of intra-class variance, inter-class variance, and class center. The PML incorporates ordinal and variational margins, along with a globally-tuned deep neural network paradigm, achieving compelling performance compared to state-of-the-art methods on three face aging datasets.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Adrian Galdran et al.
Summary: In this paper, a novel mechanism called Balanced-MixUp is proposed to address highly imbalanced medical image classification problems, it improves the balance of training data by simultaneously performing regular and balanced sampling, and experiments show promising results compared to traditional approaches.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Naina Dhingra et al.
Summary: In this study, a bidirectional GRU transformer network (BGT-Net) is proposed for scene graph generation in images, which enhances object prediction accuracy through novel object-object communication and information sharing.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021
(2021)
Proceedings Paper
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Lie Ju et al.
Summary: This study proposes a new method for recognizing retinal diseases with long-tailed data distribution, dividing data into multiple class subsets for learning, successfully addressing the imbalance learning issue. Experimental results demonstrate the flexibility and significant improvements of the method when applied to state-of-the-art techniques.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VIII
(2021)
Article
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Zhongqi Miao et al.
Summary: Camera trapping for monitoring wildlife typically requires extensive data annotation. The authors propose a hybrid system of machine learning and humans in the loop, which minimizes annotation load, improves efficiency, and enables effective model updates.
NATURE MACHINE INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Weiping Yu et al.
Summary: DSHNet is a solution proposed for the long-tail distribution problem in UAV images, utilizing Class-Biased Samplers and Bilateral Box Heads to handle tail and head classes, significantly improving the performance of tail classes and achieving state-of-the-art results on VisDrone and UAVDT datasets.
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021
(2021)
Article
Computer Science, Artificial Intelligence
Hanrui Wu et al.
Summary: The paper proposes a method for jointly capturing information and matching the distributions of source and target domains in the context of HDA. By minimizing reconstruction loss and reducing the Maximum Mean Discrepancy between domains, the method improves effectiveness and efficiency.
IEEE TRANSACTIONS ON IMAGE PROCESSING
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(2019)
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