In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, to further make the talking head generation qualified for real usage, personalized fine-tuning is usually needed. However, this process is rather computationally demanding that is unaffordable to standard users. To solve this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings.

Recent studies have shown that CLIP has achieved remarkable success in performing zero-shot inference while its fine-tuning performance is not satisfactory. In this paper, we identify that fine-tuning performance is significantly impacted by hyper-parameter choices. We examine various key hyper-parameters and empirically evaluate their impact in fine-tuning CLIP for classification tasks through a comprehensive study. We find that the fine-tuning performance of CLIP is substantially underestimated. Equipped with hyper-parameter refinement, we demonstrate CLIP itself is better or at least competitive in fine-tuning compared with large-scale supervised pre-training approaches or latest works that use CLIP as prediction targets in Masked Image Modeling. Specifically, CLIP ViT-Base/16 and CLIP ViT-Large/14 can achieve 85.7%,88.0% finetuning Top-1 accuracy on the ImageNet-1K dataset . These observations challenge the conventional conclusion that CLIP is not suitable for fine-tuning, and motivate us to rethink recently proposed improvements based on CLIP. We will release our code publicly at \url{https://github.com/LightDXY/FT-CLIP}.

This paper presents a 3D generative model that uses diffusion models to automatically generate 3D digital avatars represented as neural radiance fields. A significant challenge in generating such avatars is that the memory and processing costs in 3D are prohibitive for producing the rich details required for high-quality avatars. To tackle this problem we propose the roll-out diffusion network (Rodin), which represents a neural radiance field as multiple 2D feature maps and rolls out these maps into a single 2D feature plane within which we perform 3D-aware diffusion. The Rodin model brings the much-needed computational efficiency while preserving the integrity of diffusion in 3D by using 3D-aware convolution that attends to projected features in the 2D feature plane according to their original relationship in 3D. We also use latent conditioning to orchestrate the feature generation for global coherence, leading to high-fidelity avatars and enabling their semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing generative techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair like beards. We also demonstrate 3D avatar generation from image or text as well as text-guided editability.

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality object instances used in Copy-Paste result in more performance gain, previous works utilize object instances either from human-annotated instance segmentation datasets or rendered from 3D object models, and both approaches are too expensive to scale up to obtain good diversity. In this paper, we revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models (e.g., CLIP) and text2image models (e.g., StableDiffusion). We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable. To make such success happen, we design a data acquisition and processing framework, dubbed "X-Paste", upon which a systematic study is conducted. On the LVIS dataset, X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone. Specifically, it archives +2.6 box AP and +2.1 mask AP gains on all classes and even more significant gains with +6.8 box AP +6.5 mask AP on long-tail classes.

Accurate and timely rain prediction is crucial for decision making and is also a challenging task. This paper presents a solution which won the 2 nd prize in the Weather4cast 2022 NeurIPS competition using 3D U-Nets and EarthFormers for 8-hour probabilistic rain prediction based on multi-band satellite images. The spatial context effect of the input satellite image has been deeply explored and optimal context range has been found. Based on the imbalanced rain distribution, we trained multiple models with different loss functions. To further improve the model performance, multi-model ensemble and threshold optimization were used to produce the final probabilistic rain prediction. Experiment results and leaderboard scores demonstrate that optimal spatial context, combined loss function, multi-model ensemble, and threshold optimization all provide modest model gain. A permutation test was used to analyze the effect of each satellite band on rain prediction, and results show that satellite bands signifying cloudtop phase (8.7 um) and cloud-top height (10.8 and 13.4 um) are the best predictors for rain prediction. The source code is available at https://github.com/bugsuse/weather4cast-2022-stage2.

Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms. To study the generalization of OOD detection, in this paper, we investigate the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we also offer theoretical supports for several representative OOD detection works based on our OOD theory.

与传统的头像创建管道相反，这是一个昂贵的过程，现代生成方法直接从照片中学习数据分布，而艺术的状态现在可以产生高度的照片现实图像。尽管大量作品试图扩展无条件的生成模型并达到一定程度的可控性，但要确保多视图一致性，尤其是在大型姿势中，仍然具有挑战性。在这项工作中，我们提出了一个3D肖像生成网络，该网络可产生3D一致的肖像，同时根据有关姿势，身份，表达和照明的语义参数可控。生成网络使用神经场景表示在3D中建模肖像，其生成以支持明确控制的参数面模型为指导。尽管可以通过将图像与部分不同的属性进行对比，但可以进一步增强潜在的分离，但在非面积区域（例如，在动画表达式）时，仍然存在明显的不一致。我们通过提出一种体积混合策略来解决此问题，在该策略中，我们通过将动态和静态辐射场融合在一起，形成一个复合输出，并从共同学习的语义场中分割了两个部分。我们的方法在广泛的实验中优于先前的艺术，在自由视点中观看时，在自然照明中产生了逼真的肖像。所提出的方法还证明了真实图像以及室外卡通面孔的概括能力，在实际应用中显示出巨大的希望。其他视频结果和代码将在项目网页上提供。

尽管以前基于图的多视图聚类算法已经取得了重大进展，但其中大多数仍面临三个限制。首先，他们经常遭受高计算复杂性的困扰，这限制了他们在大规模场景中的应用。其次，他们通常在单视图级别或视图传感级别上执行图形学习，但经常忽略单视图和共识图的联合学习的可能性。第三，其中许多人依靠$ k $ - 表示光谱嵌入的离散化，这些嵌入缺乏直接使用离散群集结构直接学习图形的能力。鉴于此，本文通过统一和离散的两部分图（UDBGL）提出了一种有效的多视图聚类方法。具体而言，基于锚的子空间学习被合并为从多个视图中学习特定的二分化图，并利用双方图融合来学习具有自适应重量学习的视图 - 谐镜双分歧图。此外，施加Laplacian等级约束以确保融合的两分图具有离散的群集结构（具有特定数量的连接组件）。通过同时制定特定视图的两分图学习，视图 - 共表的两分图学习以及离散的群集结构学习到统一的目标函数中，然后设计有效的最小化算法来解决此优化问题，并直接实现离散的聚类解决方案解决方案解决方案解决方案解决方案。不需要其他分区，这特别是数据大小的线性时间复杂性。各种多视图数据集的实验证明了我们的UDBGL方法的鲁棒性和效率。

本文提出了一个简单而有效的框架蒙版，该框架将新提出的掩盖自distillation纳入对比的语言图像预处理中。掩盖自distillation的核心思想是将表示从完整的图像提取到蒙版图像预测的表示形式。这种合并享有两个重要的好处。首先，掩盖的自我验证目标是本地贴片表示学习，这与视觉对比度的互补，专注于与文本相关的表示。二，掩盖的自我验证也与视觉语言对比符合训练目标的视野对比是一致的。视觉编码器用于功能对齐，因此能够学习本地语义从该语言中获得间接监督。我们提供了专门设计的实验，并进行了全面的分析，以验证这两个好处。从经验上讲，我们表明，当MaskClip应用于各种具有挑战性的下游任务时，可以在线性探测，填充和零拍摄中取得卓越的结果，并在语言编码器的指导下取得了卓越的结果。

在3D视觉中，视觉重新定位已被广泛讨论：鉴于预构建的3D视觉图，估计查询图像的6 DOF（自由度）姿势。大规模室内环境中的重新定位可实现有吸引力的应用程序，例如增强现实和机器人导航。但是，当相机移动时，在这种环境中，外观变化很快，这对于重新定位系统来说是具有挑战性的。为了解决这个问题，我们建议一种基于虚拟视图综合方法Rendernet，以丰富有关此特定情况的数据库和完善姿势。我们选择直接渲染虚拟观点的必要全局和本地特征，而不是渲染需要高质量3D模型的真实图像，并分别将它们应用于后续图像检索和功能匹配操作中。所提出的方法在很大程度上可以改善大规模室内环境中的性能，例如，在INLOC数据集中获得7.1 \％和12.2 \％的改善。