在所需的姿势中绘制人物的图像是动漫制作中必不可少但费力的任务。在本文中，我们介绍了协作神经渲染〜（CONR）方法，以从字符表中可用的一些任意摆姿势的参考图像中创建新图像。通常，动漫人物的身体形状的高度多样性违反了像SMPL这样的现实世界人体的普遍身体模型的利用。为了克服这个困难，Conr使用紧凑且易于攻击的地标编码，以避免在管道中创建统一的紫外线映射。此外，使用特征空间跨视图密集的对应关系和翘曲在特殊设计的神经网络构建体中使用多个参考图像时，Conr的性能可以显着提高。此外，我们收集了一个字符表数据集，该数据集包含700,000多个手绘和合成的姿势图像，以促进该领域的研究。

本文报告了我们针对多媒体VICO 2022对话式头部生成挑战的解决方案，该挑战旨在根据音频和参考图像生成生动的面对面对话视频。我们的解决方案专注于使用正则化并组装高视觉质量渲染器的广义音频对手驱动器。我们仔细调整了行为的音频模型，并使用我们的前后背景融合模块进行后制作视频。我们在官方排名中的Talking Head Generation Track中获得了聆听校长曲目的第一名。我们的代码将发布。

How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical areas such as medicine and autonomous driving. The de facto approach of using the classifier's softmax outputs as a proxy for trustworthiness suffers from the over-confidence issue; while the most recent works incur problems such as additional retraining cost and accuracy versus trustworthiness trade-off. In this work, we argue that the trustworthiness of a classifier's prediction for a sample is highly associated with two factors: the sample's neighborhood information and the classifier's output. To combine the best of both worlds, we design a model-agnostic post-hoc approach NeighborAgg to leverage the two essential information via an adaptive neighborhood aggregation. Theoretically, we show that NeighborAgg is a generalized version of a one-hop graph convolutional network, inheriting the powerful modeling ability to capture the varying similarity between samples within each class. We also extend our approach to the closely related task of mislabel detection and provide a theoretical coverage guarantee to bound the false negative. Empirically, extensive experiments on image and tabular benchmarks verify our theory and suggest that NeighborAgg outperforms other methods, achieving state-of-the-art trustworthiness performance.

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.

最近的研究表明，通用风格转移的成功取得了巨大的成功，将任意视觉样式转移到内容图像中。但是，现有的方法遭受了审美的非现实主义问题，该问题引入了不和谐的模式和明显的人工制品，从而使结果很容易从真实的绘画中发现。为了解决这一限制，我们提出了一种新颖的美学增强风格转移方法，可以在美学上为任意风格产生更现实和令人愉悦的结果。具体而言，我们的方法引入了一种审美歧视者，以从大量的艺术家创造的绘画中学习通用的人类自愿美学特征。然后，合并了美学特征，以通过新颖的美学感知样式（AESSA）模块来增强样式转移过程。这样的AESSA模块使我们的Aesust能够根据样式图像的全局美学通道分布和内容图像的局部语义空间分布有效而灵活地集成样式模式。此外，我们还开发了一种新的两阶段转移培训策略，并通过两种审美正规化来更有效地训练我们的模型，从而进一步改善风格化的性能。广泛的实验和用户研究表明，我们的方法比艺术的状态综合了美学上更加和谐和现实的结果，从而大大缩小了真正的艺术家创造的绘画的差异。我们的代码可在https://github.com/endywon/aesust上找到。

求职面试通常是高风险的社交场所，需要专业和行为技巧才能令人满意。专业的工作面试培训师会根据公共标准提供有关显示行为的教育反馈。对于提高工作面试所需的行为技能，这种反馈可能会有所帮助。产生此类反馈的技术方法可能是工作面试培训的嬉戏且低调的起点。因此，我们通过基于生成的对抗网络（GAN）的方法扩展了交互式虚拟工作面试培训系统，该方法首先检测到行为弱点并随后产生个性化的反馈。为了评估生成的反馈的有用性，我们使用求职培训系统的模型进行了一项混合方法试点研究。总体研究结果表明，基于GAN的产生的行为反馈很有帮助。此外，参与者评估反馈将改善他们的工作面试绩效。

在本文中，我们介绍了纹理改革器，一个快速和通用的神经基础框架，用于使用用户指定的指导进行交互式纹理传输。挑战在三个方面：1）任务的多样性，2）引导图的简单性，以及3）执行效率。为了解决这些挑战，我们的主要思想是使用由i）全球视图结构对准阶段，ii）局部视图纹理细化阶段和III）的新的前馈多视图和多级合成程序。效果增强阶段用相干结构合成高质量结果，并以粗略的方式进行细纹细节。此外，我们还介绍了一种新颖的无学习视图特定的纹理改革（VSTR）操作，具有新的语义地图指导策略，以实现更准确的语义引导和结构保存的纹理传输。关于各种应用场景的实验结果展示了我们框架的有效性和优越性。并与最先进的交互式纹理转移算法相比，它不仅可以实现更高的质量结果，而且更加显着，也是更快的2-5个数量级。代码可在https://github.com/endywon/texture --reformer中找到。

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.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.