mlsanityData to 7 Oct 2026

Visual Transformer for Task-aware Active Learning

arXiv 2106.03801 ↗First posted 7 Jun 2021cs.CV cs.AIcited by 7

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Abstract (arXiv)

Pool-based sampling in active learning (AL) represents a key framework for an-notating informative data when dealing with deep learning models. In this paper, we present a novel pipeline for pool-based Active Learning. Unlike most previous works, our method exploits accessible unlabelled examples during training to estimate their co-relation with the labelled examples. Another contribution of this paper is to adapt Visual Transformer as a sampler in the AL pipeline. Visual Transformer models non-local visual concept dependency between labelled and unlabelled examples, which is crucial to identifying the influencing unlabelled examples. Also, compared to existing methods where the learner and the sampler are trained in a multi-stage manner, we propose to train them in a task-aware jointly manner which enables transforming the latent space into two separate tasks: one that classifies the labelled examples; the other that distinguishes the labelling direction. We evaluated our work on four different challenging benchmarks of classification and detection tasks viz. CIFAR10, CIFAR100,FashionMNIST, RaFD, and Pascal VOC 2007. Our extensive empirical and qualitative evaluations demonstrate the superiority of our method compared to the existing methods. Code available: https://github.com/razvancaramalau/Visual-Transformer-for-Task-aware-Active-Learning

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

30 of 45 references.

References · 30

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  8. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  9. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
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18 more
  1. 2017Deep Bayesian Active Learning with Image Datacited by 812
  2. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 595
  3. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  4. 2019Learning Loss for Active Learningcited by 335
  5. 2020Visual Transformers: Token-based Image Representation and Processing for Computer Visioncited by 333
  6. 2019Variational Adversarial Active Learningcited by 284
  7. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  8. 2020Contextual Diversity for Active Learningcited by 139
  9. 2016Variational Autoencoder for Deep Learning of Images, Labels and Captionscited by 137
  10. 2017Single-Shot Refinement Neural Network for Object Detectioncited by 129
  11. 2019Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Costcited by 114
  12. 2020Sequential Graph Convolutional Network for Active Learningcited by 69
  13. 2019Bayesian Batch Active Learning as Sparse Subset Approximationcited by 68
  14. 2020State-Relabeling Adversarial Active Learningcited by 60
  15. 2019Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Modelscited by 16
  16. 2020Active Learning for Bayesian 3D Hand Pose Estimationcited by 13
  17. 2019Sampling Strategies for GAN Synthetic Datacited by 11
  18. 2016Active Nearest-Neighbor Learning in Metric Spacescited by 9

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 2.

7 papers.

  1. 2021Multi-Domain Active Learning: Literature Review and Comparative Studycited by 6
  2. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  3. 2024GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noisecited by 2
  4. 2023Learning Objective-Specific Active Learning Strategies with Attentive Neural Processescited by 1
  5. 2024Image Classification with Deep Reinforcement Active Learningcited by 0
  6. 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
  7. 2025Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noisecited by 0