Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

草图在快速执行的徒手绘图时会形成直观而有力的视觉表达。我们提出了一种从场景草图中综合现实照片的方法。不需要草图和照片对，我们的框架直接以无监督的方式从随时可用的大型照片数据集中学习。为此，我们引入了一个标准化模块，该模块在训练期间通过将照片和草图转换为标准化域，即边缘地图，从而提供伪素描 - 光谱对。草图和照片之间的域间隙减少也使我们可以将它们分为两个组成部分：整体场景结构和低级视觉样式，例如颜色和纹理。利用这一优势，我们通过结合草图的结构和参考照片的视觉样式来合成照片真实的图像。关于感知相似性指标和人类感知研究的广泛实验结果表明，该方法可以从场景草图和跑赢大于最先进的照片合成基准中产生逼真的照片。我们还证明，我们的框架通过编辑相应草图的笔触来促进对照片综合的可控操作，从而比依赖于区域级编辑的以前的方法提供了更多细粒度的细节。

已知深度学习系统容易受到对抗例子的影响。特别是，基于查询的黑框攻击不需要深入学习模型的知识，而可以通过提交查询和检查收益来计算网络上的对抗示例。最近的工作在很大程度上提高了这些攻击的效率，证明了它们在当今的ML-AS-A-Service平台上的实用性。我们提出了Blacklight，这是针对基于查询的黑盒对抗攻击的新防御。推动我们设计的基本见解是，为了计算对抗性示例，这些攻击在网络上进行了迭代优化，从而在输入空间中产生了非常相似的图像查询。 Blacklight使用在概率内容指纹上运行的有效相似性引擎来检测高度相似的查询来检测基于查询的黑盒攻击。我们根据各种模型和图像分类任务对八次最先进的攻击进行评估。 Blacklight通常只有几次查询后，都可以识别所有这些。通过拒绝所有检测到的查询，即使攻击者在帐户禁令或查询拒绝之后持续提交查询，Blacklight也可以防止任何攻击完成。 Blacklight在几个强大的对策中也很强大，包括最佳的黑盒攻击，该攻击近似于效率的白色框攻击。最后，我们说明了黑光如何推广到其他域，例如文本分类。

Dry Eye Disease (DED) is one of the most common ocular diseases: over five percent of US adults suffer from DED. Tear film instability is a known factor for DED, and is thought to be regulated in large part by the thin lipid layer that covers and stabilizes the tear film. In order to aid eye related disease diagnosis, this work proposes a novel paradigm in using computer vision techniques to numerically analyze the tear film lipid layer (TFLL) spread. Eleven videos of the tear film lipid layer spread are collected with a micro-interferometer and a subset are annotated. A tracking algorithm relying on various pillar computer vision techniques is developed. Our method can be found at https://easytear-dev.github.io/.

现实世界中的数据通常显示出长尾巴的开放式（带有看不见的类）分布。实践识别系统必须在多数（头）和少数族裔（尾巴）阶级之间取得平衡，在整个分布中进行概括，并承认新颖的阶级（公开阶级）。我们将开放的长尾识别++（OLTR ++）定义为从这种自然分布的数据中学习，并优化了包括已知和开放类的平衡测试集的分类精度。 OLTR ++在一种集成算法中处理不平衡的分类，很少的学习，开放式识别和积极学习，而现有的分类方法通常仅着眼于一个或两个方面，并且在整个频谱中交付不佳。关键挑战是：1）如何在头和尾巴之间共享视觉知识，2）如何减少尾巴和开放式阶级之间的混淆，以及3）如何用学习知识积极地探索开放的课程。我们的算法OLTR ++将图像映射到特征空间，以便视觉概念可以通过记忆关联机制和学识渊博的指标（动态元元素）相互关联，这两者都尊重所封闭的见解类别的封闭世界分类并承认的新颖性打开课程。此外，我们提出了一个基于视觉记忆的主动学习方案，该方案学会以数据效率的方式识别未来扩展的开放类。在三个大规模开放的长尾数据集中，我们从Imagenet（以对象为中心），位置（以场景为中心）和MS1M（面部为中心）数据策划了三个标准基准（CIFAR-10-LT，CIFAR，CIFAR，CIFAR） -100-LT和Inaturalist-18），我们作为统一框架的方法始终展示竞争性能。值得注意的是，我们的方法还显示出积极探索开放阶级和对少数群体的公平分析的强大潜力。

交通预测对于智能交通系统的成功非常重要。深度学习模型，包括卷积神经网络和经常性神经网络，广泛应用于模型空间和时间依赖性的交通预测问题。近年来，为了模拟运输系统中的图形结构以及上下文信息，已经引入了图形神经网络，并在一系列交通预测问题中实现了最先进的性能。在本调查中，我们通过不同的图形神经网络审查了快速生长的研究机构，例如，使用不同的图形神经网络。图表卷积和图注意网络，在各种流量预测问题中，例如公路交通流量和速度预测，城市轨道交通系统的客流预测，以及乘车平台的需求预测。我们还为每个问题提供了全面的开放数据和源资源列表，并确定了未来的研究方向。据我们所知，本文是第一个探讨图形神经网络在交通预测问题中的应用的综合调查。我们还创建了一个公共github存储库，其中最新的论文，打开数据和源资源将被更新。

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.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.