High-Quality Instance Mining and Dynamic Label Assignment for Weakly Supervised Object Detection in Remote Sensing Images

Abstract

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has attracted more and more attention because its training merely relies on image-level category labels, which significantly reduces the cost of manual annotation. With the exploration of WSOD, it has obtained many promising results. However, most of the WSOD methods still have two challenges. The first challenge is that the detection results of WSOD tend to locate the significant regions of the object but not the overall object. The second challenge is that the traditional pseudo-instance label assignment strategy cannot adapt to the quality distribution change of proposals during training, which is not conducive to training a high-performance detector. To tackle the first challenge, a novel high-quality seed instance mining (HSIM) module is designed to mine high-quality seed instances. Specifically, the proposal comprehensive score (PCS) that consists of the traditional proposal score (PS) and the proposal space contribution score (PSCS) is designed as a novel metric to mine seed instances, where the PS indicates the probability that a proposal pertains to a certain category and the PSCS is calculated by the spatial correlation between top-scoring proposals, which is utilized to evaluate the wholeness with which a proposal locates an object. Consequently, the high PCS will encourage the WSOD model to mine the high-quality seed instances. To tackle the second challenge, a dynamic pseudo-instance label assignment (DPILA) strategy is developed by dynamically setting the label assignment threshold to train high-quality instances. Consequently, the DPILA can better adapt the distribution change of proposals according to the dynamic threshold during training and further promote model performance. The ablation studies verify the validity of the proposed PCS and DPILA. The comparison experiments verify that our method obtains better performance than other advanced WSOD methods on two popular RSIs datasets.