A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.

在过去十年中，深度神经网络在各种任务中取得了令人印象深刻的性能，例如自主驾驶，人脸识别和医学诊断。然而，事先作证表明，深度神经网络通过后门攻击将恶意小隐藏触发器注入模型培训，提高严重的安全威胁。要确定触发的神经元并防止反卧系攻击，我们利用福利价值并开发一种名为福利修剪（Shappruning）的新方法，该方法成功地从数据不足的情况下从模型中攻击（每级甚至没有数据） 。考虑到神经元之间的相互作用，Shappruning鉴定了少数感染的神经元（在所有神经元的1％以下），并在修剪诸如许多感染神经元后保护模型的结构和准确性。为了加速Shappruning，我们进一步提出了丢弃的阈值和$ \ epsilon $ -greedy策略以加速福利估计，使得只有几分钟的时间就可以修复中毒模型。实验证明了与现有方法相比，我们对各种攻击和任务的方法的有效性和鲁棒性。

过度平滑是一个具有挑战性的问题，这会降低深图卷积网络（GCNS）的性能。然而，用于缓解过度平滑问题的现有研究缺乏一般性或有效性。在本文中，我们分析了过度平滑问题背后的潜在问题，即特征 - 多样性退化，梯度消失和模型重量衰减。灵感来自于此，我们提出了一个简单而有效的即插即用模块，速度，缓解过度平滑。具体地，对于GCN模型的每个中间层，随机地（或基于节点度）选择节点以通过直接向非线性函数馈送它们的输入特征来跳过卷积操作。分析，1）跳过卷积操作可以防止特征失去多样性; 2）“跳过”节点使能梯度直接传递回来，从而减轻梯度消失和模型权重过腐蚀问题。为了展示Skipnode的优越性，我们对九个流行的数据集进行了广泛的实验，包括同性恋和异化图，在两个典型的任务上具有不同的图表大小：节点分类和链路预测。具体而言，1）SkipNode具有适应不同数据集和任务的各种基于GCN的模型的普遍性。 2）Skipnode优于最近最先进的反平滑插头 - 播放模块，即DropEdge和Dropnode，在不同的设置中。代码将在GitHub上公开提供。

培训广泛和深度神经网络（DNN）需要大量的存储资源，例如内存，因为在转发传播期间必须在存储器中保存中间激活数据，然后恢复以便向后传播。然而，由于硬件设计约束，诸如GPU之类的最先进的加速器（例如GPU）仅配备了非常有限的存储容量，这显着限制了在训练大规模DNN时的最大批量大小和性能加速。传统的记忆保存技术均受性能开销或受限互连带宽或特定互连技术的约束。在本文中，我们提出了一种新颖的记忆高效的CNN训练框架（称为Comet），利用错误界限的损耗压缩来显着降低训练的内存要求，以允许培训更大的模型或加速培训。不同于采用基于图像的有损压缩机（例如JPEG）的最先进的解决方案来压缩激活数据，我们的框架故意采用严格的错误控制机制来采用错误界限的损耗压缩。具体而言，我们对从改变的激活数据传播到梯度的压缩误差传播的理论分析，并经验探讨改变梯度对训练过程的影响。基于这些分析，我们优化了误报的损耗压缩，并提出了一种用于激活数据压缩的自适应误差控制方案。我们评估我们对最先进的解决方案的设计，其中包含五个广泛采用的CNN和Imagenet DataSet。实验表明，我们所提出的框架可以在基线训练中显着降低13.5倍，并分别在另一个最先进的基于压缩框架上的1.8倍，几乎没有准确性损失。

准确的动物姿势估计是了解动物行为的重要步骤，并且可能有利于许多下游应用，例如野生动物保护。以前的作用仅关注特定动物，同时忽略动物物种的多样性，限制泛化能力。在本文中，我们提出了哺乳动物动物姿势估计的第一个大规模基准的AP-10K，以促进动物姿势估计的研究。 AP-10K由10,015张图像组成，并在分类规模和54种物种之后从23个动物家庭和54种物种，标有标记和检查的高质量Keypoint注释。基于AP-10K，我们在以下三个轨道上基准代表姿态估计模型：（1）监督动物姿势估计的学习，（2）从人类姿势估计到动物姿势估计的跨域转移，和（3） - 看不见的动物的家庭间域概括。实验结果为学习的优越性从精度和泛化能力方面提供了关于从不同的动物物种的学习的优势提供的声音。它开辟了促进动物姿势估计未来研究的新方向。 AP-10K公开提供HTTPS://github.com/alexthebad/ap10k。

We consider the problem of unsupervised domain adaptation in semantic segmentation. A key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. One of the common strategies is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the category-level joint distribution. A possible consequence of such global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped, thus leading to worse segmentation results in target domain. To address this problem, we introduce a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level joint distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5 → Cityscapes and SYN-THIA → Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.