Polyps are abnormally growing tissues on the intestinal mucosal surface, often serving as precursors to cellular carcinogenesis. Accurate polyp segmentation is crucial for the diagnosis and treatment of early-stage colorectal cancer. Despite significant advancements in this field enabled by deep learning techniques, complex backgrounds in the gastrointestinal tract-such as luminal folds, air bubbles, and gastric acid secretions-still pose major challenges for existing models. These complexities hinder the models' ability to accurately perceive polyp boundaries and effectively extract contextual information. Due to the prevalent neglect of frequency-domain features in existing segmentation methods, this paper proposes SwinV2-SF-a dual-branch hybrid model integrating the spatial-frequency dualdomain network SFUNet with Swin Transformer V2. It extracts frequency-domain features through Residual Fast Fourier Transform blocks (RFFT), combines spatial and frequency features via a dynamically weighted Spatial-Frequency Attention Assembly (SFAA), and introduces the AutoDFLoss function, to resolve collectively boundary ambiguity and class imbalance. We evaluated the segmentation performance of SwinV2-SF on benchmark datasets including Kvasir-SEG and ETIS-Larib. Experimental results demonstrate that our model achieves competitive performance in terms of mDice and mIoU metrics, notably outperforming state-of-the-art models on the Kvasir-SEG dataset. The code is located at https://github.com/LeedJohn/SwinV2-SF.

The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-looping progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task.