# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### White box-based methods
#### 4.4.3 Hybrid Methods  
The hybrid sampling strategy or simultaneous approaches [36; 37; 44; 45; 57; 61; 67] combines two or more of the previously mentioned sampling strategies. Methods in this category aim at reaching a balance between multiple strategies [8]. For example, in the combination of uncertainty- and diversity-based methods, diversity can prevent instances from overlapping or becoming redundant in queries producing better sampling results [36; 67], or can overcome noisy labels [61]. Figure 10: Loss prediction module and the method to learn the loss. Image adapted from [43]. In a few other works [61, 67], the combination of AL and Semi-Supervised Learning (SSL) was addressed. Following a similar topic, Ref. [67] presented a refinement based in batch-mode, which is known as Active Semi-Supervised Learning (ASSL). The proposed method consists of firstly selecting a random set of samples from a pool and, then, adopting an efficient and flexible collaborative sampling strategy. The method integrates informative (least confidence and margin confidence sampling) and diversity (k-means) criteria from the concept of AL, as well as the confidence criterion from SSL. The former methods of SSL enabled the selection of the samples with the highest confidence (highest agreement among classifiers), considering that if samples are in the same cluster, the probability that they belong to the same class is high. The authors propose a detection model with a softmax classifier to compute the confidence scores over \(K\) classes. This allows the selection of samples with the highest Uncertainty with a probability close to 0.5 at the decision boundary, defining an upper and lower range. In [36], the authors train object detectors to compare some query methods from the unlabelled dataset, considering two stages: scoring and sampling. The informativeness measured in the scoring stage results in one score per image. In the sampling stage, the computed score (uncertainty measure) was used to select a batch of images without ignoring sample diversity. The authors empirically evaluated several scoring functions (entropy, mutual information, gradient of the output layer and bounding boxes with confidence) and two score aggregation methods (maximum and average). To evaluate diversity, the method converts unlabelled samples into feature vectors and computes similarity distance (i.e., Euclidean and Cosine Distances). K-Means, Core-Set, and Sparse Modelling were used as sampling strategies to avoid selecting highly similar instances. The result of the extensive evaluation was that mutual information with a maximum seems to offer the best trade-off between mAP and samples count. In [44], the authors focus on fine-grained sampling for AL. They proposed a method that generated queries requesting bounding box labels, selecting the most informative subset rather than entire images on a pool-based scenario. The AL framework uses Random Sampling, Mean Classification Uncertainty (i.e., choose images with high mean classification uncertainty for querying), and Coreset Greedy Diversity Sampling to solve classification problems based on image-level feature representations, demonstrating savings in labelling efforts, while ensuring a suitable OD performance. The solution was tested on two standard datasets: PASCAL VOC-12 and MS-COCO. In [37], a Batch Active Learning (BAL) method was developed, proposing a query-by-committee (QBC) for uncertainty sampling (i.e., selecting instances closer to the decision boundary) using the random forest distance function. The method computes (i) the entropy, (ii) the disagreement margin between the class with the highest estimated confidence and the second highest of an instance, and (iii) the difference in the two classes with the largest vote proportions for each class. The method includes diversity and density sampling to  
Figure 11: Eligible samples to be selected according to the sampling method.