Xuemei Li

超像素生成算法是计算机视觉中的重要研究内容. 现有方法生成的超像素大多呈近乎均匀分布, 在图像结构简单的区域会产生超像素冗余, 而在结构复杂区域则无法提供足够的超像素以表达细节. 这一问题在只使用较少超像素进行分割时尤为突出, 影响了分割的准确性. 超像素合理分配的基础是对图像结构复杂度的精确感知,分割的准确性则依赖于高质量的边界特征. 在本研究中, 我们提出了结合边界驱动与内容自适应(Boundary-Driven and Content-Adaptive, BDCA)的方法, 以应对低数量超像素分割的挑战. BDCA聚焦于两个关键点: (1)高精度边界驱动; (2)图像内容自适应. 为获取高质量边缘, 提出基于深度学习和数值拟合相结合的方法计算边界特征,该方法使用深度学习模型获取的边缘显式增强图像中的物体轮廓, 并采用二次多项式精度的曲面计算边缘特征, 从而使提取的边缘完整性好并且精度高, 为聚类和超像素生成提供有效约束. 为了根据图像的复杂度自适应调整种子点的分布, BDCA首先基于边缘特征约束进行聚类, 并基于聚类结果对图像内容进行感知, 来判定图像的简单区域和复杂区域. 在结构简单区域以尽可能少的种子点生成规则且稀疏的超像素, 在结构复杂区域采用相对多的种子点以提高分割的准确性. 特别是, 新设计的自适应距离函数, 可以根据图像的局部方差自适应调整权重, 使结构复杂区域的超像素精准地贴合物体边界. 与最先进的方法相比, 新方法生成的超像素精度高, 且规则性良好. 我们将在接受后发布代码.

Oriented object detection is an important and challenging task in the field of image processing and computer vision. The main challenge in detecting oriented objects comes from their high aspect ratio and being distributed with arbitrary orientations. Various methods have been developed to handle this issue. However, most existing works rely on time-consuming rotation and interpolation operations to align the feature representations of oriented objects. To avoid these operations, in this paper, we first introduce a simple yet effective deformable 1D directional convolution (D1DD-Conv), which implements a rotated convolution by deforming the 1D convolution kernel with horizontal and vertical offsets. Based upon this directional convolution, we then design a tri-branch convolution layer and integrate D1DD-Conv into the feature pyramid network for extracting the directional features of objects. Furthermore, we present a deep model to deal with the oriented object detection task. By allowing the offsets only along with horizontal and vertical directions, D1DD-Conv essentially corresponds to a rotated 1D convolution but without any rotation operations. This simple design is beneficial for efficiently capturing the orientation features of different oriented objects, leading to accurate prediction of the oriented bounding box of each oriented object. Some experiments on three popular datasets show that our model can achieve superior detection performance.

Semi-supervised learning (SSL) has emerged as a promising paradigm for medical image segmentation, aiming to alleviate the scarcity of high-quality annotations by combining limited labeled and abundant unlabeled data. However, existing SSL methods suffer from inherent limitations: 1) consistency regularization overly relies on enforcing prediction consistency under different perturbations, neglecting deep exploration of semantic and discriminative features; 2) pseudo-labeling methods are prone to introducing noise, which in turn undermines the stability of model training. To enable high-quality and more stable model learning, we propose a common pattern prior-driven network (CPP-Net) for semi-supervised medical image segmentation. To improve training quality, CPP-Net proposes a pattern learning mechanism that extracts each class’s core semantic information for high-quality feature learning. At its core, it is a dynamically updated common pattern bank (CP-Bank), which stores class-specific patterns learned throughout training and serves as high-quality prior knowledge for the model. By reusing CP-Bank patterns, CPP-Net reconstructs current-stage features, reduces redundant learning of shared patterns, and boosts feature robustness and discriminability. Furthermore, an information gain-driven update strategy is proposed to ensure that the CP-Bank is aligned with the historical mean of pattern distributions, preventing excessive bias toward transient local patterns. To enhance training stability, a dynamic regulation function is designed to adaptively modulate the impact of pseudo-labels according to their confidence, thereby mitigating the adverse effects of low-confidence data. Through extensive experiments on various 2D/3D medical image segmentation datasets, CPP-Net demonstrates its effectiveness and generalizability, and achieves 7.5% mean Dice improvement over SOTA.

Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density differences, leading to label ambiguity. However, the existing model treats all data equally without taking quality differences into account in the training process, resulting in noisy labels negatively impacting model training and unstable feature representations. In this paper, a data-driven alternating learning (DALE) paradigm is proposed to optimize the model's training process, achieving stable and high-precision segmentation. The paradigm focuses on two key points: (1) reducing the impact of noisy labels, and (2) calibrating unstable representations. To mitigate the negative impact of noisy labels, a loss consistency-based collaborative optimization method is proposed, and its effectiveness is theoretically demonstrated. Specifically, the label confidence parameters are introduced to dynamically adjust the influence of labels of different confidence levels during model training, thus reducing the influence of noise labels. To calibrate the learning bias of unstable representations, a distribution alignment method is proposed. This method restores the underlying distribution of unstable representations, thereby enhancing the discriminative capability of fuzzy region representations. Extensive experiments on various benchmarks and model backbones demonstrate the superiority of the DALE paradigm, achieving an average performance improvement of up to 7.16%.