Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem. This work presents an end-to-end, learning-based inverse rendering framework incorporating differentiable Monte Carlo raytracing with importance sampling. The framework takes a single image as input to jointly recover the underlying geometry, spatially-varying lighting, and photorealistic materials. Specifically, we introduce a physically-based differentiable rendering layer with screen-space ray tracing, resulting in more realistic specular reflections that match the input photo. In addition, we create a large-scale, photorealistic indoor scene dataset with significantly richer details like complex furniture and dedicated decorations. Further, we design a novel out-of-view lighting network with uncertainty-aware refinement leveraging hypernetwork-based neural radiance fields to predict lighting outside the view of the input photo. Through extensive evaluations on common benchmark datasets, we demonstrate superior inverse rendering quality of our method compared to state-of-the-art baselines, enabling various applications such as complex object insertion and material editing with high fidelity. Code and data will be made available at \url{https://jingsenzhu.github.io/invrend}.

基于草图的3D形状检索（SBSR）是一项重要但艰巨的任务，近年来引起了越来越多的关注。现有方法在限制设置中解决了该问题，而无需适当模拟真实的应用程序方案。为了模仿现实的设置，在此曲目中，我们采用了不同级别的绘图技能的业余爱好者以及各种3D形状的大规模草图，不仅包括CAD型号，而且还可以从真实对象扫描的模型。我们定义了两个SBSR任务，并构建了两个基准，包括46,000多个CAD型号，1,700个现实型号和145,000个草图。四个团队参加了这一轨道，并为这两个任务提交了15次跑步，由7个常用指标评估。我们希望，基准，比较结果和开源评估法会在3D对象检索社区中促进未来的研究。

在各种设备上部署深度学习模型已成为一个重要的话题。硬件专业化的浪潮为多维张量计算带来了一套多样化的加速度原始图。这些新的加速原始基原料以及新兴的机器学习模型带来了巨大的工程挑战。在本文中，我们提出了Tensorir，这是一种编译器抽象，用于通过这些张量计算原始素优化程序。Tensorir概括了现有机器学习编译器中使用的循环巢表示，以将张量计算作为一流的公民。最后，我们在抽象之上构建了一个端到端框架，以自动优化给定的张量计算原始图的深度学习模型。实验结果表明，Tensorir编译会自动使用给定硬件后端的张量计算原始图，并提供与跨平台的最新手工精制系统竞争性能的性能。

知识图完成最近已广泛研究，以通过主要建模图结构特征来完成三元组中的缺失元素，但对图形结构的稀疏性敏感。期望解决这一挑战的相关文本，例如实体名称和描述，充当知识图（kgs）的另一种表达形式（kgs）。已经提出了几种使用两个编码器的结构和文本消息的方法，但由于未能平衡它们之间的权重有限。并在推理期间保留结构和文本编码器，也遭受了沉重的参数。通过知识蒸馏的激励，我们将知识视为从输入到输出概率的映射，并在稀疏的kgs上提出了一个插件框架VEM2L，以将从文本和结构消息提取到统一的知识中融合知识。具体而言，我们将模型获取的知识分配为两个不重叠的部分：一个部分与训练三元组合的合适能力有关，可以通过激励两个编码者互相学习训练集来融合。另一个反映了未观察到的查询的概括能力。相应地，我们提出了一种新的融合策略，该策略由变量EM算法证明，以融合模型的概括能力，在此期间，我们还应用图形致密操作以进一步缓解稀疏的图形问题。通过结合这两种融合方法，我们最终提出了VEM2L框架。详细的理论证据以及定量和定性实验都证明了我们提出的框架的有效性和效率。

由于缺乏可用的数据集，模型和标准评估指标，因此以多模式数据为条件的现实，生动和类似人类的合成对话手势仍然是一个未解决的问题。为了解决这个问题，我们构建了人体表达式 - aauio-Text数据集，Beat，它具有76小时，高质量的，高质量的多模式数据，这些数据从30位扬声器中捕获了八种不同的情绪，用四种不同的语言，ii）32数以百万计的框架级别的情感和语义相关注释。我们对BEAT的统计分析表明，除了与音频，文本和说话者身份的已知相关性外，对话式手势与面部表情，情感和语义的相关性。基于此观察结果，我们提出了一个基线模型，即级联运动网络（CAMN），该模型由以上六种模式组成，该模式在级联的架构中建模以进行手势合成。为了评估语义相关性，我们引入了指标，语义相关性召回（SRGR）。定性和定量实验证明了指标的有效性，地面真相数据质量以及基线的最先进性能。据我们所知，BEAT是用于研究人类手势的最大运动捕获数据集，这可能有助于许多不同的研究领域，包括可控的手势合成，跨模式分析和情感手势识别。数据，代码和模型可在https://pantomatrix.github.io/beat/上获得。

盲目图像超分辨率（SR）是CV的长期任务，旨在恢复患有未知和复杂扭曲的低分辨率图像。最近的工作主要集中在采用更复杂的退化模型来模拟真实世界的降级。由此产生的模型在感知损失和产量感知令人信服的结果取得了突破性。然而，电流生成的对抗性网络结构所带来的限制仍然是显着的：处理像素同样地导致图像的结构特征的无知，并且导致性能缺点，例如扭曲线和背景过度锐化或模糊。在本文中，我们提出了A-ESRAN，用于盲人SR任务的GAN模型，其特色是基于U-NET的U-NET的多尺度鉴别器，可以与其他发电机无缝集成。据我们所知，这是第一项介绍U-Net结构作为GaN解决盲人问题的鉴别者的工作。本文还给出了对模型的多规模注意力突破的机制的解释。通过对现有作品的比较实验，我们的模型在非参考自然图像质量评估员度量上提出了最先进的水平性能。我们的消融研究表明，利用我们的鉴别器，基于RRDB的发电机可以利用多种尺度中图像的结构特征，因此与先前作品相比，更加感知地产生了感知的高分辨率图像。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.