Feature-Guided Instance Mining and Task-Aligned Focal Loss for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to achieve high-value object classification and localization using only image-level labels, and it has a wide range of applications. However, existing popular WSOD models still encounter two challenges. First, these WSOD models typically select the highest-scoring proposal as the seed instance while ignoring lower-scoring ones, resulting in some less-obvious objects being missed. Second, current models fail to ensure consistency between classification and regression, limiting the upper bound of WSOD performance. To address the first challenge, we propose a feature-guided seed instance mining (FGSIM) strategy to mine reliable seed instances. Specifically, FGSIM first selects multiple high-scoring proposals as seed instances and then leverages a feature similarity measure to mine additional seed instances among lower-scoring proposals. Furthermore, a contrastive loss is introduced to construct a credible similarity threshold for FGSIM by leveraging the consistent feature representations of instances within the same category. To address the second challenge, a task-aligned focal (TAF) loss is proposed to enforce consistency between classification and regression. Specifically, the localization difficulty score and classification difficulty score are used as weights for the regression and classification losses, respectively, thereby promoting their synchronous optimization by minimizing the TAF loss. Additionally, rotated images are incorporated into the baseline to encourage the model to make consistent predictions for objects with arbitrary orientations. Ablation studies validate the effectiveness of FGSIM, TAF loss, and their combination. Comparisons with popular models on two RSI datasets further demonstrate the superiority of our approach.