人脸图像通常以广泛的视觉量表出现。现有的面部表示通过组装有限系列的预定尺度的多尺度方案来追求处理量表变化的带宽。这种多弹药方案带来了推理负担，而预定义的量表不可避免地从真实数据中差异。取而代之的是，从数据中学习比例参数，并将其用于单发功能推理是一个不错的解决方案。为此，我们通过诉诸规模空间理论并实现两倍的设施来改革Conv层：1）Conv层从真实数据分布中学习一组尺度，每个数据分布都由Conv内核来实现； 2）该图层自动在适当的通道和位置上突出显示与输入模式量表及其存在相对应的位置。然后，我们通过堆叠改革层的层来实现分层尺度的关注，建立一种名为“比例尺注意Cons Neurnet网络”（\ textbf {scan-cnn}）的新颖风格。我们将扫描CNN应用于面部识别任务，并推动SOTA性能的前沿。当面部图像模糊时，准确性增长更为明显。同时，作为单发方案，该推断比多弹性融合更有效。与普通CNN相比，制造了一组工具，以确保对扫描CNN进行快速训练和推理成本的零增加。

最近，学习的视频压缩引起了很多关注，并显示出令人鼓舞的结果的快速发展趋势。但是，先前的作品仍然存在一些批评问题，并且在广泛使用的PSNR度量方面，具有传统压缩标准的性​​能差距。在本文中，我们提出了几种技术来有效提高性能。首先，为了解决累积错误的问题，我们将有条件的I框架作为GOP中的第一帧，该框架稳定了重建的质量并节省了比特率。其次，为了有效地提高相互预测的准确性而不增加解码器的复杂性，我们提出了一种像素到功能的运动预测方法，可以帮助我们获得高质量的运动信息。第三，我们提出了一种基于概率的熵跳过方法，该方法不仅带来了性能增长，而且大大降低了熵编码的运行时。借助这些强大的技术，本文提出了Alphavc，这是一种高性能且高效的学习视频压缩方案。据我们所知，Alphavc是第一个E2E AI编解码器，它超过了PSNR的所有常见测试数据集上最新的压缩标准VVC（-28.2％BD率节省）和MSSSSIM（-52.2％BD-rate节省），并且具有非常快速的编码（0.001x VVC）和解码（1.69x VVC）速度。

近年来，随着深度神经网络的发展，端到端优化的图像压缩已取得了重大进展，并超过了速度延伸性能的经典方法。但是，大多数基于学习的图像压缩方法是未标记的，在优化模型时不考虑图像语义或内容。实际上，人眼对不同内容具有不同的敏感性，因此还需要考虑图像内容。在本文中，我们提出了一种面向内容的图像压缩方法，该方法处理具有不同策略的不同类型的图像内容。广泛的实验表明，与最先进的端到端学习的图像压缩方法或经典方法相比，所提出的方法可实现竞争性的主观结果。

预训练的模型（PTM）已成为自然语言处理和计算机视觉下游任务的基本骨干。尽管通过在BAIDU地图上将通用PTM应用于与地理相关的任务中获得的最初收益，但随着时间的流逝，表现平稳。造成该平稳的主要原因之一是缺乏通用PTM中的可用地理知识。为了解决这个问题，在本文中，我们介绍了Ernie-Geol，这是一个地理和语言预培训模型，设计和开发了用于改善Baidu Maps的地理相关任务。 Ernie-Geol经过精心设计，旨在通过预先培训从包含丰富地理知识的异质图生成的大规模数据来学习地理语言的普遍表示。大规模现实数据集进行的广泛定量和定性实验证明了Ernie-Geol的优势和有效性。自2021年4月以来，Ernie-Geol已经在百度地图上部署在生产中，这显着受益于各种下游任务的性能。这表明Ernie-Geol可以作为各种与地理有关的任务的基本骨干。

面部反欺骗（FAS）在防止演示攻击中的人脸识别系统中起着至关重要的作用。由于身份和微不足道的方差不足，现有面部反欺骗数据集缺乏多样性，这限制了FAS模型的泛化能力。在本文中，我们提出了双重欺骗解散生成（DSDG）框架，通过“通过生成反欺骗”来解决这一挑战。根据变形AutiaceDer（VAE）中的可解释分解潜在解剖学，DSDG学习身份表示的联合分布和潜在空间中的欺骗模式表示。然后，可以从随机噪声生成大规模成对的实时和欺骗图像，以提高训练集的分集。然而，由于VAE的固有缺陷，一些产生的面部图像被部分地扭曲。这种嘈杂的样本很难预测精确的深度值，因此可能阻碍广泛使用的深度监督优化。为了解决这个问题，我们进一步引入了轻量级深度不确定性模块（DUM），减轻了噪声样本对深度不确定性学习的不利影响。 DUM在没有依赖性的情况下开发，因此可以灵活地集成与任何深度监督网络进行面部反欺骗。我们评估了提出的方法在五个流行基准上的有效性，并在测试中实现了最先进的结果。该代码可在https://github.com/jdai-cv/facex-zoo/tree/main/addition_module/dsdg中获得。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

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.

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.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.