新颖的类发现（NCD）的目的是在一个未标记的数据集中推断出新的类别，该数据集利用了包含不相交但相关类别的标签集的先验知识。现有的研究主要侧重于利用方法学层面的标签集，而不太强调标记集合本身的分析。因此，在本文中，我们从标记的集合中重新考虑了小说类发现，并关注两个核心问题：（i）给定特定的未标记集，什么样的标签集可以最好地支持新颖的类发现？ （ii）NCD的基本前提是标记的集合必须与未标记的集合有关，但是我们如何衡量这种关系？对于（i），我们提出并证实了这样的假设，即NCD可以从具有与未标记集的标签相似性的标签集中受益更多。具体而言，我们通过利用其层次结构结构来建立一个广泛而大规模的基准，在Imagenet上标记/未标记的数据集之间具有不同程度的语义相似性。作为鲜明的对比，现有的NCD基准是根据具有不同类别和图像的标签集开发的，并且完全忽略了语义关系。对于（ii），我们引入了一个数学定义，用于量化标记和未标记集之间的语义相似性。此外，我们使用此指标来确认我们提出的基准测试的有效性，并证明它与NCD性能高度相关。此外，在没有定量分析的情况下，以前的工作通常认为标签信息总是有益的。但是，违反直觉，我们的实验结果表明，使用标签可能会导致低相似性设置中的次级优势。

细分已成为计算机视觉和自然语言处理的基本领域，该领域将标签分配给每个像素/功能，以从图像/文本中提取感兴趣的区域。为了评估分割的性能，骰子和IOU指标用于衡量地面真理与预测分割之间的重叠程度。在本文中，我们建立了关于骰子/IOU指标的分割理论基础，包括贝叶斯规则和骰子/iou校准，类似于分类 - 校准或分类中的Fisher一致性。我们证明，与骰子/IOU指标相对于大多数操作损失的现有基于阈值的框架不一致，因此可能导致次优的解决方案。为了解决这一陷阱，我们提出了一个基于排名的一致框架，即rankdice/rankiou，灵感来自贝叶斯细分规则的插件规则。开发了三种具有GPU并行执行的数值算法，以在大规模和高维分段中实现所提出的框架。我们研究所提出的框架的统计特性。我们表明它是骰子 - 校准的，它的多余风险范围和收敛速度也提供了。在各种模拟示例，精细的城市景观和带有最先进的深度学习体系结构的Pascal VOC数据集中，证明了Rankdice/Mrankdice的数值有效性。

大型语言模型已被证明可以使用少量学习来实现各种自然语言任务的出色表现，这大大减少了将模型调整到特定应用程序所需的特定任务培训示例的数量。为了进一步了解量表对少量学习的影响，我们培训了一个5400亿个参数，密集激活的变压器语言模型，我们称之为“途径”语言模型棕榈。我们使用Pathways在6144 TPU V4芯片上训练了Palm，这是一种新的ML系统，可在多个TPU POD上进行高效的训练。我们通过在数百种语言理解和产生基准的基准方面实现最先进的学习结果来证明扩展的持续好处。在这些任务中，Palm 540B实现了突破性的表现，在一系列多步推理任务上表现出色，超过了最新的最新表现，并且在最近发布的Big Benchmark上表现优于平均人类表现。大量的大型基础任务显示出与模型量表的不连续改进，这意味着当我们扩展到最大模型时，性能急剧增加。 Palm在多语言任务和源代码生成方面也具有很强的功能，我们在各种基准测试中证明了这一点。我们还提供了有关偏见和毒性的全面分析，并研究了训练数据记忆的程度，相对于模型量表。最后，我们讨论与大语言模型有关的道德考虑，并讨论潜在的缓解策略。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

最近的一个令人兴奋的发展是在许多科学领域中对深度神经网络的吸收，在该领域的主要目标是对黑盒本质的结果预测。显着性测试有望解决黑匣子问题，并探索基于深度学习模型的决策过程的新颖科学见解和解释。但是，由于其黑盒性质和参数估计的未知限制分布，对神经网络的测试构成了挑战，而现有方法则需要强大的假设或过度计算。在本文中，我们得出了一个分数和两分测试，放宽了现有黑盒测试的假设和计算复杂性，并扩展了研究可能是复杂类型的数据集中感兴趣的特征集合的重要性，例如图片。一键测试估计并通过样本分割和数据扰动来基于估计和推理子集评估黑框模型。两级测试将推理子集进一步分为两个，但不需要扰动。此外，我们通过基于重复样品分裂汇总p值来开发其组合版本。通过使偏置-SD比率放气，我们建立了测试统计量的渐近零分布和II型误差方面的一致性。从数值上讲，我们在七个模拟示例和六个真实数据集上演示了提出的测试的实用性。随附的本文是我们的Python库DNN-Inprice（https://dnn-inperion.readthedocs.io/en/latest/），该库实现了所提出的测试。

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.