Co-speech gesture is crucial for human-machine interaction and digital entertainment. While previous works mostly map speech audio to human skeletons (e.g., 2D keypoints), directly generating speakers' gestures in the image domain remains unsolved. In this work, we formally define and study this challenging problem of audio-driven co-speech gesture video generation, i.e., using a unified framework to generate speaker image sequence driven by speech audio. Our key insight is that the co-speech gestures can be decomposed into common motion patterns and subtle rhythmic dynamics. To this end, we propose a novel framework, Audio-driveN Gesture vIdeo gEneration (ANGIE), to effectively capture the reusable co-speech gesture patterns as well as fine-grained rhythmic movements. To achieve high-fidelity image sequence generation, we leverage an unsupervised motion representation instead of a structural human body prior (e.g., 2D skeletons). Specifically, 1) we propose a vector quantized motion extractor (VQ-Motion Extractor) to summarize common co-speech gesture patterns from implicit motion representation to codebooks. 2) Moreover, a co-speech gesture GPT with motion refinement (Co-Speech GPT) is devised to complement the subtle prosodic motion details. Extensive experiments demonstrate that our framework renders realistic and vivid co-speech gesture video. Demo video and more resources can be found in: https://alvinliu0.github.io/projects/ANGIE

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。

了解因果关系有助于构建干预措施，以实现特定的目标并在干预下实现预测。随着学习因果关系的越来越重要，因果发现任务已经从使用传统方法推断出潜在的因果结构从观察数据到深度学习涉及的模式识别领域。大量数据的快速积累促进了具有出色可扩展性的因果搜索方法的出现。因果发现方法的现有摘要主要集中在基于约束，分数和FCM的传统方法上，缺乏针对基于深度学习的方法的完美分类和阐述，还缺乏一些考虑和探索因果关系的角度来探索因果发现方法范式。因此，我们根据变量范式将可能的因果发现任务分为三种类型，并分别给出三个任务的定义，定义和实例化每个任务的相关数据集以及同时构建的最终因果模型，然后审查不同任务的主要因果发现方法。最后，我们从不同角度提出了一些路线图，以解决因果发现领域的当前研究差距，并指出未来的研究方向。

在本文中，我们通过整合具有离散的傅立叶变换（DFT）的复杂值和实值卷积神经网络（CNN）来提出一个新的EEG信号分类框架。所提出的神经网络架构由一个复杂值的卷积层，两个实值卷积层和三个完全连接的层组成。我们的方法可以有效利用DFT中包含的相信息。我们使用两个模拟的EEG信号和一个基准数据集验证我们的方法，并将其与两个广泛使用的框架进行比较。与对基准数据集进行分类的现有方法相比，我们的方法大大减少了所使用的参数的数量并提高了准确性，并显着提高了对模拟的EEG信号进行分类的性能。

CVPR中的农业视觉挑战是全球研究人员打破计算机视觉和农业部门之间边界的最著名和竞争性挑战之一，目的是从空中图像中识别农业模式。在本文中，我们建议解决CVPR 2022的第三次农业视觉挑战的解决方案。我们利用数据预处理方案和几种基于变压器的模型以及数据增强技术来达到0.582的MIOU，以实现第二名在这个挑战中。

人脸识别是计算机视觉中最受欢迎和最长的主题之一。随着最近的深度学习技术和大规模数据集的发展，深刻的面貌识别取得了显着的进展，并广泛用于许多现实世界应用。给定自然图像或视频帧作为输入，端到端的深脸识别系统输出面部特征以识别。为此，典型的端到端系统通常具有三个关键元素：面部检测，面部对准和面部表示。面部检测定位在图像或框架中的面部。然后，继续进行面对准以将面部校准到规范视图并将它们裁剪到归一化的像素尺寸。最后，在面部表示的阶段，从对准的面部提取歧视特征以进行识别。如今，所有三个要素都是通过深度卷积神经网络的技术实现的。在本调查文章中，我们对最近的端到端深刻识别的每个元素的进步进行了全面的审查，因为蓬勃发展学习技巧极大地提高了它们的能力。首先，我们概述了端到端深表识的概述。然后，我们分别审查每个元素的前进，涵盖许多方面，例如迄今的算法设计，评估指标，数据集，性能比较，对未来研究的有希望和有希望的方向。通过这项调查，我们希望在两个方面提出贡献：首先，读者可以方便地识别子类别中具有相当强大的基础风格的方法，以进一步探索;其次，人们还可以采用合适的方法来从划痕建立最先进的端到端面部识别系统。

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

This paper introduces a learned hierarchical B-frame coding scheme in response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2023. We address specifically three issues, including (1) B-frame coding, (2) YUV 4:2:0 coding, and (3) content-adaptive variable-rate coding with only one single model. Most learned video codecs operate internally in the RGB domain for P-frame coding. B-frame coding for YUV 4:2:0 content is largely under-explored. In addition, while there have been prior works on variable-rate coding with conditional convolution, most of them fail to consider the content information. We build our scheme on conditional augmented normalized flows (CANF). It features conditional motion and inter-frame codecs for efficient B-frame coding. To cope with YUV 4:2:0 content, two conditional inter-frame codecs are used to process the Y and UV components separately, with the coding of the UV components conditioned additionally on the Y component. Moreover, we introduce adaptive feature modulation in every convolutional layer, taking into account both the content information and the coding levels of B-frames to achieve content-adaptive variable-rate coding. Experimental results show that our model outperforms x265 and the winner of last year's challenge on commonly used datasets in terms of PSNR-YUV.

A major goal of multimodal research is to improve machine understanding of images and text. Tasks include image captioning, text-to-image generation, and vision-language representation learning. So far, research has focused on the relationships between images and text. For example, captioning models attempt to understand the semantics of images which are then transformed into text. An important question is: which annotation reflects best a deep understanding of image content? Similarly, given a text, what is the best image that can present the semantics of the text? In this work, we argue that the best text or caption for a given image is the text which would generate the image which is the most similar to that image. Likewise, the best image for a given text is the image that results in the caption which is best aligned with the original text. To this end, we propose a unified framework that includes both a text-to-image generative model and an image-to-text generative model. Extensive experiments validate our approach.