Recent Advances of Continual Learning in Computer Vision: An Overview

Qu, Haoxuan; Rahmani, Hossein; Xu, Li; Williams, Bryan; Liu, Jun

doi:10.48550/arxiv.2109.11369

Cited by 16 publications

(16 citation statements)

References 171 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid methods prevent more forgetting: The best performing solutions to the class-incremental problem in object detection involved a combination of techniques to avoid catastrophic forgetting. This outcome agrees with the findings from other computer vision tasks [102] and corroborates with the fact that even the brain has multiple ways to prevent subtle task interference [103]. One key point common to most hybrid methods was the fine-tuning on new classes given the representation of old categories using pseudo-labels or replay samples.…”

Section: Trends and Research Directionssupporting

confidence: 85%

Continual Object Detection: A review of definitions, strategies, and challenges

Menezes¹,

Moura²,

Alves³

et al. 2022

Preprint

View full text Add to dashboard Cite

The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. Its focus has been mainly on incremental classification tasks. We believe that research in continual object detection deserves even more attention due to its vast range of applications in robotics and autonomous vehicles. This scenario is more complex than conventional classification given the occurrence of instances of classes that are unknown at the time, but can appear in subsequent tasks as a new class to be learned, resulting in missing annotations and conflicts with the background label. In this review, we analyze the current strategies proposed to tackle the problem of class-incremental object detection. Our main contributions are:(1) a short and systematic review of the methods that propose solutions to traditional incremental object detection scenarios; (2) A comprehensive evaluation of the existing approaches using a new metric to quantify the stability and plasticity of each technique in a standard way; (3) an overview of the current trends within continual object detection and a discussion of possible future research directions.

show abstract

Section: Trends and Research Directionssupporting

confidence: 85%

Continual Object Detection: A review of definitions, strategies, and challenges

Menezes¹,

Moura²,

Alves³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…30, we highlight the performance gain of ViTs as compared to CNNs for out of distribution detection to inspire medical imaging researchers who wish to explore this area. Another possible direction is to explore the recent advancements in continual learning [437] to effectively mitigate the issue of domains shift using ViTs. Few preliminary efforts have been made to explore this direction [438]; however, the work is still in its infancy and requires further attention from the community.…”

Section: Domain Adaptation and Out-of-distribution Detectionmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

View full text Add to dashboard Cite

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

show abstract

“…Meanwhile, Bateni et al (2022) and Chen et al (2020b) enable few-shot and efficient continual learning respectively, with the assistance of unlabeled data. A recent survey paper (Qu et al, 2021) also provides a good summary of current achievements in this field.…”

Section: Related Workmentioning

confidence: 99%

Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Chen¹,

Liu²,

Chang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forgetting issue could be mitigated by storing historical data for replay, which yet would cause memory overheads as well as imbalanced prediction updates. To address this dilemma, we propose to leverage "free" external unlabeled data querying in continual learning. We first present a CIL with Queried Unlabeled Data (CIL-QUD) scheme, where we only store a handful of past training samples as anchors and use them to query relevant unlabeled examples each time. Along with new and past stored data, the queried unlabeled are effectively utilized, through learning-without-forgetting (LwF) regularizers and class-balance training. Besides preserving model generalization over past and current tasks, we next study the problem of adversarial robustness for CIL-QUD. Inspired by the recent success of learning robust models with unlabeled data, we explore a new robustness-aware CIL setting, where the learned adversarial robustness has to resist forgetting and be transferred as new tasks come in continually. While existing options easily fail, we show queried unlabeled data can continue to benefit, and seamlessly extend CIL-QUD into its robustified versions, RCIL-QUD. Extensive experiments demonstrate that CIL-QUD achieves substantial accuracy gains on CIFAR-10 and CIFAR-100, compared to previous state-of-the-art CIL approaches. Moreover, RCIL-QUD establishes the first strong milestone for robustness-aware CIL. Codes are available in https://github.com/VITA-Group/CIL-QUD.

show abstract

Recent Advances of Continual Learning in Computer Vision: An Overview

Cited by 16 publications

References 171 publications

Continual Object Detection: A review of definitions, strategies, and challenges

Continual Object Detection: A review of definitions, strategies, and challenges

Transformers in Medical Imaging: A Survey

Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning

Contact Info

Product

Resources

About