The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, which can be plugged into generic polyp detection models to filter out false positive predictions. This is realized by generative learning of the polyp manifold with normalizing flows and rejecting false positives through density estimation. Compared to supervised classification, this anomaly detection paradigm achieves better data efficiency and robustness in open-world settings. Extensive experiments demonstrate a promising false positive suppression in both retrospective and prospective validation. In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis. The dataset and code will be publicly available at http://endoboost.miccai.cloud.

The effective application of contrastive learning technology in natural language processing tasks shows the superiority of contrastive learning in text analysis tasks. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. Since it is difficult to construct contrastive objects in multi-label multi-classification tasks, there are few contrastive losses for multi-label multi-classification text classification. In this paper, we propose five contrastive losses for multi-label multi-classification tasks. They are Strict Contrastive Loss (SCL), Intra-label Contrastive Loss (ICL), Jaccard Similarity Contrastive Loss (JSCL), and Jaccard Similarity Probability Contrastive Loss (JSPCL) and Stepwise Label Contrastive Loss (SLCL). We explore the effectiveness of contrastive learning for multi-label multi-classification tasks under different strategies, and provide a set of baseline methods for contrastive learning techniques on multi-label classification tasks. We also perform an interpretability analysis of our approach to show how different contrastive learning methods play their roles. The experimental results in this paper demonstrate that our proposed contrastive losses can bring some improvement for multi-label multi-classification tasks. Our work reveal how to "appropriately" change the contrastive way of contrastive learning is the key idea to improve the adaptability of contrastive learning in multi-label multi-classification tasks.

3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert curve-based cross-dimensionality distillation approach that facilitates the knowledge of 3D networks to improve the performance of 2D networks. The proposed Hilbert Distillation (HD) method preserves the structural information via the Hilbert curve, which maps high-dimensional (>=2) representations to one-dimensional continuous space-filling curves. Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations. We further propose a Variable-length Hilbert Distillation (VHD) method to dynamically shorten the walking stride of the Hilbert curve in activation feature areas and lengthen the stride in context feature areas, forcing the 2D networks to pay more attention to learning from activation features. The proposed algorithm outperforms the current state-of-the-art distillation techniques adapted to cross-dimensionality distillation on two classification tasks. Moreover, the distilled 2D networks by the proposed method achieve competitive performance with the original 3D networks, indicating the lightweight distilled 2D networks could potentially be the substitution of cumbersome 3D networks in the real-world scenario.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

在本文中，我们研究了从许多嘈杂的随机线性测量值中恢复低级别基质的问题。我们考虑以下设置的设置，即基地矩阵的等级是未知的，并使用矩阵变量的过度指定的分组表示，其中全局最佳解决方案过拟合，并且与基础基础真相不符。然后，我们使用梯度下降和小的随机初始化解决了相关的非凸问题。我们表明，只要测量运算符能够满足受限的等轴测特性（RIP），其等级参数缩放具有地面真相矩阵等级，而不是使用过度指定的矩阵变量进行缩放，那么梯度下降迭代就会在特定的轨迹上朝向地面。 - 正确矩阵并在适当停止时获得了几乎信息理论上的最佳恢复。然后，我们提出了一种基于共同持有方法的有效的早期停止策略，并表明它可以检测到几乎最佳的估计量。此外，实验表明，所提出的验证方法也可以有效地用于图像恢复，并具有深层图像先验，从而使图像过度参与了深层网络。

事件参数提取（EAE）的目的是从文本中提取具有给定角色的参数，这些参数已在自然语言处理中得到广泛研究。以前的大多数作品在具有专用神经体系结构的特定EAE数据集中取得了良好的性能。鉴于，这些架构通常很难适应具有各种注释模式或格式的新数据集/方案。此外，他们依靠大规模标记的数据进行培训，由于大多数情况下的标签成本高，因此无法获得培训。在本文中，我们提出了一个具有变异信息瓶颈的多格式转移学习模型，该模型利用了信息，尤其是新数据集中EAE现有数据集中的常识。具体而言，我们引入了一个共享特定的及时框架，以从具有不同格式的数据集中学习格式共享和格式特定的知识。为了进一步吸收EAE的常识并消除无关的噪音，我们将变异信息瓶颈整合到我们的体系结构中以完善共享表示。我们在三个基准数据集上进行了广泛的实验，并在EAE上获得新的最先进的性能。

准确估计电池的健康状况（SOH）有助于防止电池供电的应用出乎意料的失败。随着减少新电池模型培训的数据需求的优势，转移学习（TL）是一种有前途的机器学习方法，该方法应用了从源电池中学到的知识，该方法具有大量数据。但是，尽管这些是成功的TL的关键组成部分，但很少讨论源电池模型是否合理以及可以传输的信息的哪一部分的确定。为了应对这些挑战，本文通过利用时间动态来协助转移学习，提出了一种可解释的基于TL的SOH估计方法，该方法由三个部分组成。首先，在动态时间扭曲的帮助下，放电时间序列的时间数据被同步，从而产生了循环同步时间序列的翘曲路径，这些时间序列负责使周期上的容量降解。其次，从周期同步时间序列的空间路径中检索的规范变体用于在源电池和目标电池之间进行分布相似性分析。第三，当分布相似性在预定义的阈值范围内时，通过从源SOH估计模型转移常见的时间动力学来构建一个综合目标SOH估计模型，并用目标电池的残留模型补偿错误。通过广泛使用的开源基准数据集，通过根平方误差评估的提议方法的估计误差高达0.0034，与现有方法相比，准确性提高了77％。

对比学习在视频表示学习中表现出了巨大的潜力。但是，现有方法无法充分利用短期运动动态，这对于各种下游视频理解任务至关重要。在本文中，我们提出了运动敏感的对比度学习（MSCL），该学习将光学流捕获的运动信息注入RGB帧中，以增强功能学习。为了实现这一目标，除了剪辑级全球对比度学习外，我们还开发了局部运动对比度学习（LMCL），具有两种模式的框架级对比目标。此外，我们引入流动旋转增强（FRA），以生成额外的运动除件负面样品和运动差分采样（MDS）以准确筛选训练样品。对标准基准测试的广泛实验验证了该方法的有效性。以常用的3D RESNET-18为骨干，我们在UCF101上获得了91.5 \％的前1个精度，而在视频分类中进行了一些v2的v2，以及65.6 \％的top-1 top-1召回ucf1011对于视频检索，特别是改善了最新的。

三维（3D）综合肾脏结构（IRS）分割在临床实践中很重要。随着深度学习技术的发展，提出了许多专注于医学图像细分的强大框架。在这一挑战中，我们利用了NNU-NET框架，这是医学图像分割的最新方法。为了减少肿瘤标签的异常预测，我们将肿瘤标签的轮廓正则化（CR）丢失与骰子丢失和横向渗透丢失相结合，以改善这种现象。

近年来，随着新颖的策略和应用，神经网络一直在迅速扩展。然而，尽管不可避免地会针对关键应用程序来解决这些挑战，例如神经网络技术诸如神经网络技术中仍未解决诸如神经网络技术的挑战。已经尝试通过用符号表示来表示和嵌入域知识来克服神经网络计算中的挑战。因此，出现了神经符号学习（Nesyl）概念，其中结合了符号表示的各个方面，并将常识带入神经网络（Nesyl）。在可解释性，推理和解释性至关重要的领域中，例如视频和图像字幕，提问和推理，健康信息学和基因组学，Nesyl表现出了有希望的结果。这篇综述介绍了一项有关最先进的Nesyl方法的全面调查，其原理，机器和深度学习算法的进步，诸如Opthalmology之类的应用以及最重要的是该新兴领域的未来观点。