神经量渲染能够在自由观看中的人类表演者的照片真实效果图，这是沉浸式VR/AR应用中的关键任务。但是，这种做法受到渲染过程中高计算成本的严重限制。为了解决这个问题，我们提出了紫外线量，这是一种新方法，可以实时呈现人类表演者的可编辑免费视频视频。它将高频（即非平滑）的外观与3D体积分开，并将其编码为2D神经纹理堆栈（NTS）。光滑的紫外线量允许更小且较浅的神经网络获得3D的密度和纹理坐标，同时在2D NT中捕获详细的外观。为了编辑性，参数化的人类模型与平滑纹理坐标之间的映射使我们可以更好地对新型姿势和形状进行更好的概括。此外，NTS的使用启用了有趣的应用程序，例如重新启动。关于CMU Panoptic，ZJU MOCAP和H36M数据集的广泛实验表明，我们的模型平均可以在30fps中呈现960 * 540张图像，并具有可比的照片现实主义与先进方法。该项目和补充材料可从https://github.com/fanegg/uv-volumes获得。

接地的情况识别（GSR），即识别图像中的显着活动（或动词）类别（例如，购买）和检测所有相应的语义角色（例如，代理商和货物），是朝向“人类”的重要步骤事件理解。由于每个动词与特定的语义角色相关联，所以所有现有的GSR方法都采用了一个两级框架：在第一阶段预测动词并检测第二阶段的语义角色。然而，两个阶段存在明显的缺点：1）由于在日常活动中的阶级内变化和高阶间相似性，对物体识别的广泛使用的跨熵（XE）损耗在动词分类中不足。 2）以自回归方式检测到所有语义角色，这不能模拟不同角色之间的复杂语义关系。为此，我们为GSR提出了一种新的Situformer，其包括粗略的动词模型（CFVM）和基于变压器的名词模型（TNM）。 CFVM是一种两步动词预测模型：具有XE损耗培训的粗粒模型首先提出了一组动词候选，然后用三态损失培训的细粒度模型重新排名这些候选者，并使用增强的动词功能（不仅可分离但也是歧视的）。 TNM是一种基于变换器的语义角色检测模型，其并行检测所有角色。由于变压器解码器的全局关系建模能力和灵活性，TNM可以完全探索角色的统计依赖性。对挑战性SWIG基准测试的广泛验证表明，Situformer在各种指标下实现了一种新的最先进的性能。代码可在https://github.com/kellyiss/situformer中获得。

神经辐射场（NERF）最近获得了令人印象深刻的新型观点综合能力的普及。本文研究了幻觉的nerf问题：即，在一组旅游形象的一天的不同时间恢复现实的nerf。现有解决方案采用NERF具有可控外观嵌入，以在各种条件下呈现新颖的视图，但不能以看不见的外观呈现视图 - 一致的图像。为了解决这个问题，我们提出了一种用于构建幻觉的nerf的端到端框架，称为H-nerf。具体地，我们提出了一种外观幻觉模块，以处理时变的外观，并将其转移到新颖的视图中。考虑到旅游图像的复杂遮挡，引入防遮挡模块以准确地分解静态受体的静态对象。合成数据和真实旅游照片集合的实验结果表明，我们的方法不仅可以幻觉所需的外观，还可以从不同视图中呈现无遮挡图像。项目和补充材料可在https://rover-xingyu.github.io/h-nerf/上获得。

人类视力能够从整个场景中捕获部分整个分层信息。本文介绍了Visual解析器（VIP），它明确地构造了与变压器的等层次结构。 VIP将视觉表示分为两个级别，零件级别和整个级别。每个部分的信息代表整个内部的几个独立向量的组合。为了模拟两个级别的表示，我们首先通过注意机制将整体信息从整体编码为部分向量，然后将零件向量内的全局信息解码回到整个表示中。通过使用所提出的编码器 - 解码器交互迭代地解析两个级别，模型可以逐渐改进两个级别上的特征。实验结果表明，VIP可以在三个主要任务中实现非常竞争的性能。分类，检测和实例分割。特别是，它可以通过对象检测的大边缘超越先前的最先进的CNN主干。 VIP系列的小型型号为7.2美元，参数为$ 7.2 \ times $ 10.9 \ times $更少的拖鞋可以与最大的resnext-101-64 $ \ times $ 4d的resne（x）t家族相对表现。可视化结果还表明，学习部分对预测类具有高度信息，使VIP比以前的基本架构更可说明。代码可在https://github.com/kevin-ssy/vip上获得。

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive to collect. In contrast, common unsupervised image-to-image translation methods are not applicable to font generation, as they often define style as the set of textures and colors. In this work, we propose a robust deformable generative network for unsupervised font generation (abbreviated as DGFont++). We introduce a feature deformation skip connection (FDSC) to learn local patterns and geometric transformations between fonts. The FDSC predicts pairs of displacement maps and employs the predicted maps to apply deformable convolution to the low-level content feature maps. The outputs of FDSC are fed into a mixer to generate final results. Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts. To distinguish different styles, we train our model with a multi-task discriminator, which ensures that each style can be discriminated independently. In addition to adversarial loss, another two reconstruction losses are adopted to constrain the domain-invariant characteristics between generated images and content images. Taking advantage of FDSC and the adopted loss functions, our model is able to maintain spatial information and generates high-quality character images in an unsupervised manner. Experiments demonstrate that our model is able to generate character images of higher quality than state-of-the-art methods.

Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physical systems. Nevertheless, the majority of neural operator applications has thus far been data-driven, which neglects the intrinsic preservation of fundamental physical laws in data. In this paper, we introduce a novel integral neural operator architecture, to learn physical models with fundamental conservation laws automatically guaranteed. In particular, by replacing the frame-dependent position information with its invariant counterpart in the kernel space, the proposed neural operator is by design translation- and rotation-invariant, and consequently abides by the conservation laws of linear and angular momentums. As applications, we demonstrate the expressivity and efficacy of our model in learning complex material behaviors from both synthetic and experimental datasets, and show that, by automatically satisfying these essential physical laws, our learned neural operator is not only generalizable in handling translated and rotated datasets, but also achieves state-of-the-art accuracy and efficiency as compared to baseline neural operator models.