Object Detection via Active Learning Strategy Based on Saliency of Local Features and Posterior Probability

Active learning is a strategy that involves the deliberate selection of training samples with significant discriminative features, to formulate a more efficient learning paradigm. While active learning has seen success in image classification, its application in object detection remains complex due to the multiple candidate object instances in an image and the interference of background regions on these instances. To tackle the challenge of sample selection in active learning for large object recognition datasets, this paper introduces a active learning method based on local feature saliency and posterior probability. Initially, the method selects active learning samples with discriminative saliency by clustering the multi-scale feature maps of the processed samples by self-attention weights. Subsequently, it calculates the posterior entropy based on the posterior probability of the current model’s candidate instances. This is combined with the entropy encoding of feature points corresponding to instances in the cluster to further select uncertain samples in the object set. On the COCO dataset, our method demonstrates superior overall performance in the learning curve compared to existing object detection baselines. Moreover, under the same training configuration, the final detection accuracy shows a notable improvement of at least 2.36%, thus confirming the effectiveness of our method in enhancing learning and detection performance.