节点注入对图神经网络（GNN）的攻击已作为一种实际的攻击场景而引起了人们的注意，攻击者会注入恶意节点，而不是修改节点功能或边缘以降低GNN的性能。尽管节点注射攻击最初取得了成功，但我们发现，通过防御方法，可以通过防御方法和限制其在实践中限制其攻击性能，从而很容易将注射的节点与原始正常节点区分开。为了解决上述问题，我们致力于伪装节点注入攻击，即伪装注入恶意节点（结构/属性）是对防御方法似乎合理/不察觉的普通淋巴结。图形数据的非欧亚人性质和缺乏人类的先验性质给伪装上伪装的形式化，实施和评估带来了巨大挑战。在本文中，我们首先提出并制定了从注射节点围绕的自我网络的忠诚度和多样性中注入的节点的伪装。然后，我们为节点注射攻击（即Cana）设计了一个对抗性伪装框架，以改善伪装，同时确保攻击性能。进一步设计了几种用于图形伪装的新型指标，以进行全面的评估。实验结果表明，当将现有的节点注入攻击方法与我们提出的CANA框架配置时，针对防御方法的攻击性能以及节点伪装将显着改善。

文本匹配是信息检索和自然语言处理的基本技术。文本匹配任务共享确定两个给定文本之间关系的相同范例。这些关系因任务而异，例如〜在文档检索中相关性，释义识别中的语义一致性和所回答的可回答判断。但是，文本匹配的基本信号保留在有限范围中，即〜精确匹配，语义匹配和推理匹配。理想情况下，良好的文本匹配模型可以学会捕获和汇总这些信号，以实现不同的匹配任务以实现竞争性能，而最近的最新文本匹配模型，例如〜预训练的语言模型（PLM）很难概括。这是因为在特定于任务的数据集上的端到端监督学习使模型过分强调了数据示例偏置和特定于任务的信号，而不是基本的匹配信号。为了克服这个问题，我们采用了专业化的将军培训策略，并将其称为比赛推出。在专业阶段，对不同匹配任务的描述映射到一些提示令牌。在概括阶段，匹配模型通过接受各种匹配任务的培训来探索基本匹配信号。高不同的匹配任务避免了模型拟合特定任务的数据偏差，因此该模型可以专注于学习基本匹配信号。同时，在第一步中获得的提示令牌有助于模型区分不同的特定任务匹配信号。公共数据集上的实验结果表明，匹配点可以提高PLM在文本匹配中的多任务概括能力，并产生更好的内域多任务，外域多任务和新任务适应性性能由以前的微调范式训练的特定于任务模型。

卡尔曼滤波器广泛用于对象跟踪，其中过程和测量噪声通常被认为是准确的已知和恒定的。然而，确切的已知和常量假设并不总是在实践中保持。例如，当LIDAR用于跟踪非合作目标时，在不同距离和天气条件下测量噪声不同。另外，过程噪声随对象的运动状态而变化，尤其是当跟踪对象是行人时，并且过程噪声更频繁地改变。本文提出了一种新的估计校正校正闭环估计方法，用于在线估算卡尔曼滤波器过程和测量噪声协方差矩阵。首先，我们将噪声协方差矩阵分解为元素分布矩阵和噪声强度，并改善Sage滤波器以估计元素分布矩阵。其次，我们提出了一种校准方法来准确地诊断噪声强度偏差。然后，我们提出了一种正确的方法来在线自适应地校正噪声强度。第三，在假设系统是可检测的情况下，在数学上证明了所提出的方法的无偏偏差和收敛。仿真结果证明了所提出的方法的有效性和可靠性。最后，我们将建议的方法应用于多对LIDAR的跟踪并在官方Kitti服务器上进行评估。在基提步行者多元object跟踪排行榜上提出的方法（http://www.cvlibs.net/datasets /kitti/eval_tracking.php）超越了使用激光雷达的所有现有方法，证明了在实际应用中的方法的可行性。这项工作提供了一种提高卡尔曼滤波器和多功能跟踪性能的新方法。

有条件的生成模型旨在学习数据和标签的基础联合分布，以实现有条件的数据生成。其中，辅助分类器生成的对抗网络（AC-GAN）已被广泛使用，但遭受了生成样品的阶层内多样性的问题。本文指出的基本原因是，AC-GAN的分类器是生成器 - 静脉器，因此不能为发电机提供接近联合分布的信息指导，从而最小化条件熵，从而减少了阶级内的阶级。多样性。在这种理解的推动下，我们提出了一个具有辅助判别分类器（ADC-GAN）的新型条件gan，以解决上述问题。具体而言，提出的辅助判别分类器通过识别真实数据的类标签和生成的数据而成为生成器感知。我们的理论分析表明，即使没有原始歧视者，发电机也可以忠实地学习联合分布，从而使拟议的ADC-GAN可靠，可适应该系数超参数的价值和GAN损失的选择，并在训练过程中稳定。关于合成和现实世界数据集的广泛实验结果表明，与基于最新的分类器和基于基于投影的条件gan相比，有条件生成建模中ADC-GAN的优势。

最近，基于转换的自我监督学习已经应用于生成的对抗性网络（GANS），通过引入静止学习环境来缓解争夺者中的灾难性遗忘。然而，现有的自我监督GAN中的单独自我监督任务导致目标不一致，因为它们的自我监督分类器对发电机分配不可知。为了解决这个问题，我们提出了一种新颖的自我监督GaN，通过自我监督通过数据转换增强GaN标签（真实或假），将GaN任务统一了GAN任务。具体地，原始鉴别器和自我监督分类器统一到标签增强的鉴别器中，预测增强标签要知道每个转换下的发电机分配和数据分布，然后提供它们之间的差异以优化发电机。从理论上讲，我们证明了最佳发生器可以收敛以复制实际数据分布。凭经验，我们表明，该方法显着优异地优于先前的自我监督和数据增强GAN在基准数据集中的生成建模和代表学习。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

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.

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.