Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, previous works attempted to utilize the abundant unlabeled parallel corpora to produce additional training data with pseudo labels. In this paper, we demonstrate a significant gap between parallel data and real QE data: for QE data, it is strictly guaranteed that the source side is original texts and the target side is translated (namely translationese). However, for parallel data, it is indiscriminate and the translationese may occur on either source or target side. We compare the impact of parallel data with different translation directions in QE data augmentation, and find that using the source-original part of parallel corpus consistently outperforms its target-original counterpart. Moreover, since the WMT corpus lacks direction information for each parallel sentence, we train a classifier to distinguish source- and target-original bitext, and carry out an analysis of their difference in both style and domain. Together, these findings suggest using source-original parallel data for QE data augmentation, which brings a relative improvement of up to 4.0% and 6.4% compared to undifferentiated data on sentence- and word-level QE tasks respectively.

持续学习（CL）依次学习像人类这样的新任务，其目标是实现更好的稳定性（S，记住过去的任务）和可塑性（P，适应新任务）。由于过去的培训数据不可用，因此探索培训示例中S和P的影响差异很有价值，这可能会改善对更好的SP的学习模式。受影响函数的启发（如果），我们首先研究了示例通过添加扰动来示例体重和计算影响推导的影响。为了避免在神经网络中Hessian逆的存储和计算负担，我们提出了一种简单而有效的METASP算法，以模拟IF计算中的两个关键步骤，并获得S-和P-Aware示例的影响。此外，我们建议通过解决双目标优化问题来融合两种示例影响，并获得对SP Pareto最优性的融合影响。融合影响可用于控制模型的更新并优化排练的存储。经验结果表明，我们的算法在任务和类别基准CL数据集上都显着优于最先进的方法。

众所周知，深度学习方法是渴望数据的，它需要大量标记的样本。不幸的是，大量的交互式样品标记工作极大地阻碍了深度学习方法的应用，尤其是对于需要异质样本的3D建模任务。为了减轻对FA \ c {C} ADS的3D建模的数据注释的工作，本文提出了一种半监督的对抗识别策略，该策略嵌入了逆程序建模中。从纹理LOD-2（详细级别）模型开始，我们使用经典的卷积神经网络来识别来自图像补丁的类型并估算Windows的参数。然后将窗口类型和参数组装到程序语法中。一个简单的程序引擎是在现有的3D建模软件中构建的，产生了细粒的窗户几何形状。为了从一些标记的样品中获得有用的模型，我们利用生成对抗网络以半监督的方式训练特征提取器。对抗训练策略还可以利用未标记的数据，使训练阶段更加稳定。使用公开可用的FA \ c {C} ADE图像数据集的实验表明，在同一网络结构下，提出的培训策略可以提高分类精度的提高约10％，参数估计提高了50％。此外，在针对具有不同fa \ c {c} ADE样式的不同数据测试时，性能提高更为明显。

我们研究了GaN调理问题，其目标是使用标记数据将普雷雷尼的无条件GaN转换为条件GaN。我们首先识别并分析这一问题的三种方法 - 从头开始​​，微调和输入重新编程的条件GaN培训。我们的分析表明，当标记数据的数量很小时，输入重新编程执行最佳。通过稀缺标记数据的现实世界情景，我们专注于输入重编程方法，并仔细分析现有算法。在识别出先前输入重新编程方法的一些关键问题之后，我们提出了一种名为INREP +的新算法。我们的算法INREP +解决了现有问题，具有可逆性神经网络的新颖用途和正面未标记（PU）学习。通过广泛的实验，我们表明Inrep +优于所有现有方法，特别是当标签信息稀缺，嘈杂和/或不平衡时。例如，对于用1％标记数据调节CiFar10 GaN的任务，Inrep +实现了82.13的平均峰值，而第二个最佳方法达到114.51。

混合是一种数据增强方法，通过混合一对输入数据来生成新数据点。虽然混合通常会改善预测性能，但它有时会降低性能。在本文中，我们首先通过理论上和经验分析混合算法来确定这种现象的主要原因。要解决此问题，我们提出了一种简单但有效的重定标记算法，专为混合而提出了Genlabel。特别是，GenLabel通过使用生成模型学习类条件数据分布，帮助混合算法正确标记混合样本。通过广泛的理论和实证分析，我们表明混合，当与Genlabel一起使用时，可以有效地解决上述现象，从而提高泛化性能和对抗鲁棒性。

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.

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.