Recent neural radiance field (NeRF) representation has achieved great success in the tasks of novel view synthesis and 3D reconstruction. However, they suffer from the catastrophic forgetting problem when continuously learning from streaming data without revisiting the previous training data. This limitation prohibits the application of existing NeRF models to scenarios where images come in sequentially. In view of this, we explore the task of incremental learning for neural radiance field representation in this work. We first propose a student-teacher pipeline to mitigate the catastrophic forgetting problem. Specifically, we iterate the process of using the student as the teacher at the end of each incremental step and let the teacher guide the training of the student in the next step. In this way, the student network is able to learn new information from the streaming data and retain old knowledge from the teacher network simultaneously. Given that not all information from the teacher network is helpful since it is only trained with the old data, we further introduce a random inquirer and an uncertainty-based filter to filter useful information. We conduct experiments on the NeRF-synthetic360 and NeRF-real360 datasets, where our approach significantly outperforms the baselines by 7.3% and 25.2% in terms of PSNR. Furthermore, we also show that our approach can be applied to the large-scale camera facing-outwards dataset ScanNet, where we surpass the baseline by 60.0% in PSNR.

Accurate airway extraction from computed tomography (CT) images is a critical step for planning navigation bronchoscopy and quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). The existing methods are challenging to sufficiently segment the airway, especially the high-generation airway, with the constraint of the limited label and cannot meet the clinical use in COPD. We propose a novel two-stage 3D contextual transformer-based U-Net for airway segmentation using CT images. The method consists of two stages, performing initial and refined airway segmentation. The two-stage model shares the same subnetwork with different airway masks as input. Contextual transformer block is performed both in the encoder and decoder path of the subnetwork to finish high-quality airway segmentation effectively. In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage. Then the predictions of the two-stage method are merged as the final prediction. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analysis demonstrate that our proposed method extracted much more branches and lengths of the tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

神经网络（NNS）和决策树（DTS）都是机器学习的流行模型，但具有相互排斥的优势和局限性。为了带来两个世界中的最好，提出了各种方法来明确或隐式地集成NN和DTS。在这项调查中，这些方法是在我们称为神经树（NTS）的学校中组织的。这项调查旨在对NTS进行全面审查，并尝试确定它们如何增强模型的解释性。我们首先提出了NTS的彻底分类学，该分类法表达了NNS和DTS的逐步整合和共同进化。之后，我们根据NTS的解释性和绩效分析，并建议解决其余挑战的可能解决方案。最后，这项调查以讨论有条件计算和向该领域的有希望的方向进行讨论结束。该调查中审查的论文列表及其相应的代码可在以下网址获得：https：//github.com/zju-vipa/awesome-neural-trees

最近，视觉变形金刚（VITS）正在快速发展，并开始挑战计算机视觉（CV）领域的卷积神经网络（CNNS）的统治。利用用于更换卷积的硬编码的感应偏差的通用变压器架构，VITS已经超过了CNN，尤其是数据充足的情况。然而，VITS容易超过小型数据集，因此依靠大规模的预训练，这花费了巨大的时间。在本文中，我们努力通过引入CNNS的归纳偏见来解放VITS，通过返回vits，同时保留其网络架构以获得更高的上限并设置更合适的优化目标。首先，代理CNN基于具有感应偏差的给定韦尔设计。然后提出了一种自举训练算法，共同优化了重量共享的药剂和vit，在此期间，VIT学习来自代理的中间特征的诱导偏差。具有有限培训数据的CiFar-10/100和Imagenet-1k上的广泛实验表明，令人鼓舞的结果，感应偏差有助于VITS更快地收敛，甚至更少的参数。

ProtoPNet and its follow-up variants (ProtoPNets) have attracted broad research interest for their intrinsic interpretability from prototypes and comparable accuracy to non-interpretable counterparts. However, it has been recently found that the interpretability of prototypes can be corrupted due to the semantic gap between similarity in latent space and that in input space. In this work, we make the first attempt to quantitatively evaluate the interpretability of prototype-based explanations, rather than solely qualitative evaluations by some visualization examples, which can be easily misled by cherry picks. To this end, we propose two evaluation metrics, termed consistency score and stability score, to evaluate the explanation consistency cross images and the explanation robustness against perturbations, both of which are essential for explanations taken into practice. Furthermore, we propose a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and substantial discussions to uncover the interpretability of existing ProtoPNets. Experiments demonstrate that our method achieves significantly superior performance to the state-of-the-arts, under both the conventional qualitative evaluations and the proposed quantitative evaluations, in both accuracy and interpretability. Codes are available at https://github.com/hqhQAQ/EvalProtoPNet.

To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS are trained on the large dataset with marks, which may not fit well with the small dataset without marks, causing the convergence to be not ideal. In this study, we propose a Feature Fusion two-stream framework (FF2) to bridge the gap. Specifically, one stream leverages a pre-trained language model to capture the semantic feature, while another auxiliary module captures the feature at hand. We also modify the computation of multi-head attention to encourage communication among heads. Then, two features with different perspectives are aggregated to fuse information and enhance context awareness. Without additional data, the experimental results on the popular benchmark IWSLT demonstrate that FF2 achieves new SOTA performance, which verifies that our approach is effective.

文本到图像生成旨在生成与给定文本一致的真实图像。先前的作品主要通过堆叠生成器 - 歧义器对进行多个对抗训练，主要采用多阶段体系结构，在该培训中，用于提供发电指导的文本语义在所有阶段都保持静态。这项工作认为，每个阶段的文本特征应根据历史阶段的状态（即历史阶段的文本和图像特征）进行自适应重新组合，以在粗到精细的生成过程中提供多样化和准确的语义指导。因此，我们提出了一种新颖的动力学语义演化gan（DSE-GAN），以在新颖的单一对抗性多阶段体系结构下重新构成每个阶段的文本特征。具体而言，我们设计（1）动态语义演化（DSE）模块，该模块首先汇总了历史图像特征以总结生成反馈，然后动态选择在每个阶段重新组装的单词，并通过动态地组装它们增强或抑制不同的粒度子空间的语义。 （2）单个对抗性多阶段体系结构（SAMA），通过消除复杂的多个对抗训练要求扩展了先前的结构，因此可以允许更多的文本图像相互作用阶段，并最终促进DSE模块。我们进行了全面的实验，并表明DSE-GAN在两个广泛使用的基准分别（即CUB-200和MSCOCO）上获得了7.48 \％和37.8％的相对FID。

在本文中，我们考虑了颜色加式双相机系统，并提出了一个端到端的卷积神经网络，以有效且具有成本效益的方式使图像对齐和融合图像。我们的方法将跨域和跨尺度图像作为输入，因此综合了HR着色结果，以促进单相机成像系统中时空分辨率和色彩深度之间的权衡。与以前的着色方法相反，我们的功能可以适应具有独特时空分辨率的色彩和单色相机，从而使实际应用中的灵活性和鲁棒性。我们方法的关键要素是一个跨相机比对模块，该模块生成跨域图像对齐的多尺度对应关系。通过在各种数据集和多个设置上进行广泛的实验，我们验证了方法的灵活性和有效性。值得注意的是，我们的方法始终取得了实质性改进，即在最新方法上，大约10dB PSNR增益。代码为：https：//github.com/indigopurple/ccdc

原型零件网络（Protopnet）引起了广泛的关注，并增加了许多随访研究，因为它的自我解释特性可解释人工智能（XAI）。但是，当直接在视觉变压器（VIT）骨架上应用原始网络时，学到的原型存在“分心”问题：它们具有相对较高的可能性，即被背景激活，并且对前景的关注较少。建模长期依赖性的强大能力使得基于变压器的Protopnet难以专注于原型部分，从而严重损害了其固有的解释性。本文提出了原型零件变压器（ProtoPformer），以适当有效地应用基于原型的方法，并使用VIT进行可解释的图像识别。提出的方法介绍了根据VIT的建筑特征捕获和突出目标的代表性整体和部分特征的全局和局部原型。采用了全球原型，以提供对象的全球视图，以指导本地原型集中在前景上，同时消除背景的影响。之后，明确监督局部原型，以专注于它们各自的原型视觉部分，从而提高整体可解释性。广泛的实验表明，我们提出的全球和本地原型可以相互纠正并共同做出最终决策，这些决策分别忠实，透明地从整体和地方的角度缔合过程。此外，ProtoPformer始终取得优于基于原型的原型基线（SOTA）的卓越性能和可视化结果。我们的代码已在https://github.com/zju-vipa/protopformer上发布。

我们考虑估计与I.I.D的排名$ 1 $矩阵因素的问题。高斯，排名$ 1 $的测量值，这些测量值非线性转化和损坏。考虑到非线性的两种典型选择，我们研究了从随机初始化开始的此非convex优化问题的天然交流更新规则的收敛性能。我们通过得出确定性递归，即使在高维问题中也是准确的，我们显示出算法的样本分割版本的敏锐收敛保证。值得注意的是，虽然无限样本的种群更新是非信息性的，并提示单个步骤中的精确恢复，但算法 - 我们的确定性预测 - 从随机初始化中迅速地收敛。我们尖锐的非反应分析也暴露了此问题的其他几种细粒度，包括非线性和噪声水平如何影响收敛行为。从技术层面上讲，我们的结果可以通过证明我们的确定性递归可以通过我们的确定性顺序来预测我们的确定性序列，而当每次迭代都以$ n $观测来运行时，我们的确定性顺序可以通过$ n^{ - 1/2} $的波动。我们的技术利用了源自有关高维$ m $估计文献的遗留工具，并为通过随机数据的其他高维优化问题的随机初始化而彻底地分析了高阶迭代算法的途径。