# Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
###### Abstract  
Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning  
designed strategy and promote the performance with the selected samples interactively. The above iterative training process between optimization and annotator is the primary active learning mechanism, and human annotations exist in each training interaction. Consequently, the essence of active learning is the _Human-in-the-Loop_ (HITL) computing systems, where human expertise is joint in the computer-based systems [5]. Humans (such as doctors in clinical diagnosis) are part of the intelligent system and participate in the model training process by providing judgments or domain-knowledge that influence the final output of the system [6]. More details are introduced in Algorithm 1 and other surveys [5,6]. ```
Input : A labeled data pool \(L\), an unlabeled data pool \(\mathcal{U}\), annotators \(A\). Output : A well-trained model \(\mathcal{M}\) with least labeling cost. \(\mathcal{M}\) initialization; repeat  Train the model \(\mathcal{M}\) with \(L\);  Obtain the representation \(\mathcal{R}\) of all samples \(x\in\mathcal{U}\), \(\mathcal{R}=\mathcal{M}(x)\);  Query the top-K informative samples \(\mathcal{K}\) via selection strategies, according to the representation \(\mathcal{R}\);  Annotate the samples \(\mathcal{K}\) and obtain the labels \(Y^{\mathcal{K}}=A(\mathcal{K})\);  Update \(L=L\cup\{\mathcal{K},Y^{\mathcal{K}}\}\), update \(\mathcal{U}=\mathcal{U}/\mathcal{K}\). untilEnd conditions;
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**Algorithm 1**The pool-based active learning workflow  
[PERSON]'s active learning literature survey [4] systematically summarized classic active learning methods in 2004. More basic definitions and formulations can be found in this survey [4]. Active learning has been rapidly growing and booming with various novel methodologies, applications and challenges in recent years. [PERSON]'s survey provided the basic theory for active learning, especially the traditional AL. Differently, our work focuses on deep learning-based active learning theories in computer vision tasks, which is named deep active learning in this paper. Apart from this, we summarize the latest surveys [6, 7, 8, 9] about deep learning in Table 1. Previous surveys systematically introduced the deep active learning in many fields, such as CV and NLP. After studied the existing surveys about deep active learning and their references, we found that there was not any review designed for CV researcher. Hence, we decided to re-organize existing works and introduce latest research from a CV-related perspective in this manuscript. The biggest difference between this manuscript and above-mentioned works is that the expected audience of this review are researchers who are working in computer vision but willing to utilize deep active learning methods to solve CV problems. Active learning is still new to them. As such, we organized this manuscript from the perspective of a CV researcher, and introduced the deep active learning from CV-related methodologies and corresponding applications. This CV-friendly survey is our new contribution to the community. The remainder of this review is as follows: First, we argue that it is necessary to introduce the basic concepts and methodologies of traditional active learning for the newcomers. Then, Section 2 introduces the three basic candidate selection strategies in active learning and give responding examples, and then we detail the pool-based strategy.