在非欧几里得空间上卷积成功之后，在有关图形的各种任务上也验证了相应的合并方法。但是，由于固定的压缩配额和逐步合并设计，这些层次池方法仍然遭受局部结构损害和次优问题的困扰。在这项工作的启发下，我们提出了一种层次的合并方法，即SEP解决这两个问题。具体而言，在不分配特定层的压缩配额的情况下，全局优化算法旨在生成一次集群分配矩阵以一次汇总。然后，我们介绍了在环和网格合成图的重建中先前方法中局部结构损害的例证。除SEP外，我​​们还将分别设计两个分类模型，分别用于图形分类和节点分类。结果表明，SEP在图形分类基准上优于最先进的图形合并方法，并在节点分类上获得了卓越的性能。

利用图像生成模型的最新进展，现有的可控面图像合成方法能够生成具有某些可控性的高保真图像，例如控制生成的面部图像的形状，表达，纹理和姿势。但是，这些方法集中在2D图像生成模型上，这些模型容易在大表达和姿势变化下产生不一致的面部图像。在本文中，我们提出了一个新的基于NERF的条件3D面部合成框架，该框架可以通过从3D脸先进的3D面部施加显式3D条件来对生成的面部图像进行3D可控性。其核心是有条件的生成占用场（CGOF），可有效地强制生成的面部形状，以使其对给定的3D形态模型（3DMM）网格进行。为了准确控制合成图像的细粒3D面部形状，我们还将3D地标损耗以及体积翘曲损失纳入我们的合成算法中。实验验证了所提出的方法的有效性，该方法能够生成高保真的面部图像，并显示出比基于2D的最新可控制的面部合成方法更精确的3D可控性。在https://keqiangsun.github.io/projects/cgof上查找代码和演示。

良好的研究努力致力于利用股票预测中的深度神经网络。虽然远程依赖性和混沌属性仍然是在预测未来价格趋势之前降低最先进的深度学习模型的表现。在这项研究中，我们提出了一个新的框架来解决这两个问题。具体地，在将时间序列转换为复杂网络方面，我们将市场价格系列转换为图形。然后，从映射的图表中提取参考时间点和节点权重之间的关联的结构信息以解决关于远程依赖性和混沌属性的问题。我们采取图形嵌入式以表示时间点之间的关联作为预测模型输入。节点重量被用作先验知识，以增强时间关注的学习。我们拟议的框架的有效性通过现实世界股票数据验证，我们的方法在几个最先进的基准中获得了最佳性能。此外，在进行的交易模拟中，我们的框架进一步获得了最高的累积利润。我们的结果补充了复杂网络方法在金融领域的现有应用，并为金融市场中决策支持的投资应用提供了富有识别的影响。

What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these intrinsic properties, we may render roses of different sizes and shapes, in different poses, and under different lighting conditions. In this work, we build a generative model that learns to capture such object intrinsics from a single image, such as a photo of a bouquet. Such an image includes multiple instances of an object type. These instances all share the same intrinsics, but appear different due to a combination of variance within these intrinsics and differences in extrinsic factors, such as pose and illumination. Experiments show that our model successfully learns object intrinsics (distribution of geometry, texture, and material) for a wide range of objects, each from a single Internet image. Our method achieves superior results on multiple downstream tasks, including intrinsic image decomposition, shape and image generation, view synthesis, and relighting.

随着增强的焦点和虚拟现实应用（XR）来说，可以对可以将物体从图像和视频升力到适合各种相关3D任务的表示的算法。 XR设备和应用程序的大规模部署意味着我们不能仅仅依赖于监督学习，因为收集和注释现实世界中无限各种物体的数据是不可行的。我们提出了一种弱监督的方法，能够将物体的单个图像分解成形状（深度和正规），材料（反射率，反射率和发光）和全局照明参数。对于培训，该方法仅依赖于训练对象的粗略初始形状估计来引导学习过程。这种形状监督可以例如从预先预制的深度网络或 - 从传统的结构 - 来自运动管道中的普罗维尔或 - 更慷慨地实现。在我们的实验中，我们表明该方法可以将2D图像成功地将2D图像成功渲染为分解的3D表示并推广到未经证明的对象类别。由于缺乏频繁的评估因缺乏地面真理数据而困难，我们还介绍了一种允许定量评估的照片 - 现实的合成测试集。

从2D图像中学习可变形的3D对象通常是一个不适的问题。现有方法依赖于明确的监督来建立多视图对应关系，例如模板形状模型和关键点注释，这将其在“野外”中的对象上限制了。建立对应关系的一种更自然的方法是观看四处移动的对象的视频。在本文中，我们介绍了Dove，一种方法，可以从在线可用的单眼视频中学习纹理的3D模型，而无需关键点，视点或模板形状监督。通过解决对称性诱导的姿势歧义并利用视频中的时间对应关系，该模型会自动学会从每个单独的RGB框架中分解3D形状，表达姿势和纹理，并准备在测试时间进行单像推断。在实验中，我们表明现有方法无法学习明智的3D形状，而无需其他关键点或模板监督，而我们的方法在时间上产生了时间一致的3D模型，可以从任意角度来对其进行动画和呈现。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

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.