Modeling noise transition matrix is a kind of promising method for learning with label noise. Based on the estimated noise transition matrix and the noisy posterior probabilities, the clean posterior probabilities, which are jointly called Label Distribution (LD) in this paper, can be calculated as the supervision. To reliably estimate the noise transition matrix, some methods assume that anchor points are available during training. Nonetheless, if anchor points are invalid, the noise transition matrix might be poorly learned, resulting in poor performance. Consequently, other methods treat reliable data points, extracted from training data, as pseudo anchor points. However, from a statistical point of view, the noise transition matrix can be inferred from data with noisy labels under the clean-label-domination assumption. Therefore, we aim to estimate the noise transition matrix without (pseudo) anchor points. There is evidence showing that samples are more likely to be mislabeled as other similar class labels, which means the mislabeling probability is highly correlated with the inter-class correlation. Inspired by this observation, we propose an instance-specific Label Distribution Regularization (LDR), in which the instance-specific LD is estimated as the supervision, to prevent DCNNs from memorizing noisy labels. Specifically, we estimate the noisy posterior under the supervision of noisy labels, and approximate the batch-level noise transition matrix by estimating the inter-class correlation matrix with neither anchor points nor pseudo anchor points. Experimental results on two synthetic noisy datasets and two real-world noisy datasets demonstrate that our LDR outperforms existing methods.

自动化的腹部多器官分割是计算机辅助诊断腹部器官相关疾病的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了显着的成功，但由于腹部器官的不同大小以及它们之间的含糊界限，腹部器官的准确分割仍然具有挑战性。在本文中，我们提出了一个边界感知网络（BA-NET），以分段CT扫描和MRI扫描进行腹部器官。该模型包含共享编码器，边界解码器和分割解码器。两个解码器都采用了多尺度的深度监督策略，这可以减轻可变器官尺寸引起的问题。边界解码器在每个量表上产生的边界概率图被用作提高分割特征图的注意。我们评估了腹部多器官细分（AMOS）挑战数据集的BA-NET，并获得了CT扫描的多器官分割的平均骰子分数为89.29 $ \％$，平均骰子得分为71.92 $ \％$ \％$ \％ MRI扫描。结果表明，在两个分割任务上，BA-NET优于NNUNET。

肾脏结构细分是计算机辅助诊断基于手术的肾癌的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了显着的成功，但由于肾脏肿瘤的尺寸可变，肾脏肿瘤及其周围环境之间的歧义范围可变，因此对计算机层析造影血管造影（CTA）图像的肾脏结构的准确分割仍然具有挑战性。 。在本文中，我们在CTA扫描中提出了一个边界感知网络（BA-NET），以分段肾脏，肾脏肿瘤，动脉和静脉。该模型包含共享编码器，边界解码器和分割解码器。两个解码器都采用了多尺度的深度监督策略，这可以减轻肿瘤大小可变的问题。边界解码器在每个量表上产生的边界概率图被用作提高分割特征图的注意。我们在肾脏解析（KIPA）挑战数据集上评估了BA-NET，并通过使用4倍的交叉验证来实现CTA扫描的肾脏结构细分的平均骰子得分为89.65 $ \％$。结果证明了BA-NET的有效性。

颈动脉血管壁分割是在计算机辅助诊断动脉粥样硬化中的至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了显着的成功，但由于注释有限和异构动脉，对磁共振（MR）图像上颈动脉壁（MR）图像的准确分割仍然具有挑战性。在本文中，我们在3D MR图像上提出了一个半监督标签的传播框架，以分段管腔，正常容器壁和动脉粥样硬化血管壁。通过插值提供的注释，我们获得了3D连续标签，用于训练3D分割模型。借助训练有素的模型，我们生成了未标记切片的伪标签，以将其纳入模型训练。然后，我们使用整个MR扫描和传播标签来重新培养分割模型并改善其稳健性。我们评估了颈动脉血管墙分割和动脉粥样硬化诊断（COSMOS）挑战数据集上的标签传播框架，并在测试数据集中获得了83.41 \％的Quanm分数，这使在线评估排行榜上获得了1-ST的位置。结果证明了拟议框架的有效性。

手动注释医学图像是高度主观的，导致不可避免和巨大的注释偏见。深度学习模型可能超过各种任务的人类性能，但它们也可能模仿或放大这些偏差。虽然我们可以有多个注释器并融化它们的注释来减少随机错误，但我们无法使用这种策略来处理因注释器偏好引起的偏差。在本文中，我们突出了对医学图像分割任务的注释相关偏差问题，并提出了涉及涉及的注释分配学习（PADL）框架来解决它从解开注入者的偏好使用分配学习的随机误差的偏好来解决它由于不仅产生元分割，而且产生每个注释器的分割。在此框架下，随机误差建模（SEM）模块估计元分割图和平均随机错误映射，以及一系列人类偏好建模（HPM）模块估计每个注释器的分段和相应的随机误差。我们在具有不同的成像方式的两个医学图像基准上进行了评估了我们的PADL框架，这些模型由多个医疗专业人员注释，并在所有五种医学图像分割任务上取得了有希望的表现。

域间隙主要由可变的医学图像质量引起的构成，这是训练实验室中的分割模型与应用训练的模型在未见临床数据之间的路径上的主要障碍。为了解决这个问题，已经提出了域泛化方法，但是通常使用静态卷积，并且灵活性较低。在本文中，我们提出了一个基于域和内容自适应卷积（DCAC）的多源域概括模型，以分割不同模式的医学图像。具体而言，我们设计了域自适应卷积（DAC）模块和内容自适应卷积（CAC）模块，并将两者都合并到编码器解码器中。在DAC模块中，动态卷积头是根据输入的预测域代码进行的，以使我们的模型适应看不见的目标域。在CAC模块中，动态卷积头在全局图像特征上进行条件，以使我们的模型适应测试图像。我们针对基线的DCAC模型和针对前列腺分割，COVID-19病变分段和视频杯/视盘分段任务的四种最先进的域概括方法评估了DCAC模型。我们的结果不仅表明所提出的DCAC模型在每个分割任务上都优于所有竞争方法，而且还证明了DAC和CAC模块的有效性。代码可在\ url {https://git.io/dcac}上获得。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.