In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.02%, 1.4%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an effective BiSTNet to explore colors of reference exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the reference exemplars in deep feature space to explore color information from reference exemplars. Then, to better propagate the colors of reference exemplars into each frame and avoid the inaccurate matches colors from exemplars we develop a simple yet effective bidirectional temporal feature fusion module to better colorize each frame. We note that there usually exist color-bleeding artifacts around the boundaries of the important objects in videos. To overcome this problem, we further develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process for better performance. In addition, we develop a multi-scale recurrent block to progressively colorize frames in a coarse-to-fine manner. Extensive experimental results demonstrate that the proposed BiSTNet performs favorably against state-of-the-art methods on the benchmark datasets. Our code will be made available at \url{https://yyang181.github.io/BiSTNet/}

气道分割对于检查，诊断和预后的肺部疾病至关重要，而其手动描述则不当。为了减轻这种耗时且潜在的主观手动程序，研究人员提出了从计算机断层扫描（CT）图像自动分割气道的方法。但是，一些小型气道分支（例如，支气管和终末支气管）显着加剧了通过机器学习模型的自动分割难度。特别是，气道分支中体素值和严重的数据失衡的方差使计算模块容易导致不连续和假阴性预测。注意机制表明了分割复杂结构的能力，而模糊逻辑可以减少特征表示的不确定性。因此，由模糊注意力层给出的深度注意力网络和模糊理论的整合应该是升级的解决方案。本文提出了一种有效的气道分割方法，包括一个新型的模糊注意力神经网络和全面的损失函数，以增强气道分割的空间连续性。深层模糊集由特征图中的一组体素和可学习的高斯成员功能制定。与现有的注意机制不同，所提出的特异性模糊注意力解决了不同渠道中异质特征的问题。此外，提出了一种新的评估指标来评估气道结构的连续性和完整性。该方法的效率已通过在包括精确的09和LIDC数据集在内的开放数据集上进行测试，以及我们的内部Covid-19和纤维化肺病数据集证明了这一建议的效率。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

已经开发了许多本体论，即描述逻辑（DL）知识库，以提供有关各个领域的丰富知识，并且其中许多基于ALC，即原型和表达的DL或其扩展。探索ALC本体论的主要任务是计算语义范围。符号方法可以保证声音和完整的语义需要，但对不一致和缺失信息敏感。为此，我们提出了一个模糊的ALC本体神经推理器Falcon。 Falcon使用模糊逻辑运算符为任意ALC本体论生成单个模型结构，并使用多个模型结构来计算语义索引。理论结果表明，保证猎鹰是计算ALC本体学语义索引的声音和完整算法。实验结果表明，Falcon不仅可以近似推理（不完整的本体理由）和chanseansissist的推理（因本体不一致的推理），还可以通过结合ALC本体的背景知识来改善生物医学领域的机器学习。

组织分割是病理检查的主要主机，而手动描述则过于繁重。为了协助这一耗时和主观的手动步骤，研究人员已经设计了自动在病理图像中分割结构的方法。最近，自动化机器和基于深度学习的方法主导了组织分割研究。但是，大多数基于机器和深度学习的方法都是使用大量培训样本进行监督和开发的，其中PixelWise注释很昂贵，有时无法获得。本文通过将端到端的深层混合模型与有限的指标集成以获取准确的语义组织分割，从而引入了一种新颖的无监督学习范式。该约束旨在在计算优化函数期间集中深层混合模型的组成部分。这样做，可以大大减少当前无监督学习方法中常见的多余或空的班级问题。通过对公共和内部数据集的验证，拟议的深度约束高斯网络在组织细分方面取得了更好的性能（Wilcoxon签名级测试）更好的性能（平均骰子得分分别为0.737和0.735），具有改善与其他现有的无监督分割方法相比。此外，该方法与完全监督的U-NET相比，提出的方法具有相似的性能（P值> 0.05）。

鉴于攻击测试集和大型模型参数上现有的中国语法误差校正模型的稳健性不佳，本文使用知识蒸馏的方法来压缩模型参数并提高模型的反攻击能力。在数据方面，攻击测试集是通过将干扰集成到标准评估数据集中来构建的，并且通过攻击测试集评估模型鲁棒性。实验结果表明，蒸馏小型模型可以确保在减少模型参数数量的条件下确保性能并提高训练速度，并对攻击测试集获得最佳效果，并且鲁棒性得到显着提高。

我们提出了一个开放域的社交聊天机器人Chirpy Cardinal。为了既有信息又有信息，我们的机器人以一种真实的，情感上的方式与用户聊天。通过将受控的神经产生与脚手架，手写的对话整合在一起，我们让用户和机器人都轮流推动对话，从而产生引人入胜且流利的体验。Chirpy Cardinal部署在Alexa奖Socialbot Grand Challenge的第四次迭代中，每天处理数千次对话，在9个机器人中排名第二，平均用户评级为3.58/5。

从点云中检测3D对象是一项实用但充满挑战的任务，最近引起了越来越多的关注。在本文中，我们提出了针对3D对象检测的标签引导辅助训练方法（LG3D），该方法是增强现有3D对象检测器的功能学习的辅助网络。具体而言，我们提出了两个新型模块：一个标签 - 通道诱导器，该模块诱导器将框架中的注释和点云映射到特定于任务的表示形式和一个标签 - 知识式插曲器，该标签知识映射器有助于获得原始特征以获得检测临界表示。提出的辅助网络被推理丢弃，因此在测试时间没有额外的计算成本。我们对室内和室外数据集进行了广泛的实验，以验证我们的方法的有效性。例如，我们拟议的LG3D分别在SUN RGB-D和SCANNETV2数据集上将投票人员分别提高了2.5％和3.1％的地图。