Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone which has been much neglected before. We systematically explore vision Transformers as diffusion learners for various generative tasks. With our improvements the performance of vanilla ViT-based backbone (IU-ViT) is boosted to be on par with traditional U-Net-based methods. We further provide a hypothesis on the implication of disentangling the generative backbone as an encoder-decoder structure and show proof-of-concept experiments verifying the effectiveness of a stronger encoder for generative tasks with ASymmetriC ENcoder Decoder (ASCEND). Our improvements achieve competitive results on CIFAR-10, CelebA, LSUN, CUB Bird and large-resolution text-to-image tasks. To the best of our knowledge, we are the first to successfully train a single diffusion model on text-to-image task beyond 64x64 resolution. We hope this will motivate people to rethink the modeling choices and the training pipelines for diffusion-based generative models.

The recent success of pre-trained 2D vision models is mostly attributable to learning from large-scale datasets. However, compared with 2D image datasets, the current pre-training data of 3D point cloud is limited. To overcome this limitation, we propose a knowledge distillation method for 3D point cloud pre-trained models to acquire knowledge directly from the 2D representation learning model, particularly the image encoder of CLIP, through concept alignment. Specifically, we introduce a cross-attention mechanism to extract concept features from 3D point cloud and compare them with the semantic information from 2D images. In this scheme, the point cloud pre-trained models learn directly from rich information contained in 2D teacher models. Extensive experiments demonstrate that the proposed knowledge distillation scheme achieves higher accuracy than the state-of-the-art 3D pre-training methods for synthetic and real-world datasets on downstream tasks, including object classification, object detection, semantic segmentation, and part segmentation.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Understanding objects is a central building block of artificial intelligence, especially for embodied AI. Even though object recognition excels with deep learning, current machines still struggle to learn higher-level knowledge, e.g., what attributes an object has, and what can we do with an object. In this work, we propose a challenging Object Concept Learning (OCL) task to push the envelope of object understanding. It requires machines to reason out object affordances and simultaneously give the reason: what attributes make an object possesses these affordances. To support OCL, we build a densely annotated knowledge base including extensive labels for three levels of object concept (category, attribute, affordance), and the causal relations of three levels. By analyzing the causal structure of OCL, we present a baseline, Object Concept Reasoning Network (OCRN). It leverages causal intervention and concept instantiation to infer the three levels following their causal relations. In experiments, OCRN effectively infers the object knowledge while following the causalities well. Our data and code are available at https://mvig-rhos.com/ocl.

The generation of Chinese fonts has a wide range of applications. The currently predominated methods are mainly based on deep generative models, especially the generative adversarial networks (GANs). However, existing GAN-based models usually suffer from the well-known mode collapse problem. When mode collapse happens, the kind of GAN-based models will be failure to yield the correct fonts. To address this issue, we introduce a one-bit stroke encoding and a few-shot semi-supervised scheme (i.e., using a few paired data as semi-supervised information) to explore the local and global structure information of Chinese characters respectively, motivated by the intuition that strokes and characters directly embody certain local and global modes of Chinese characters. Based on these ideas, this paper proposes an effective model called \textit{StrokeGAN+}, which incorporates the stroke encoding and the few-shot semi-supervised scheme into the CycleGAN model. The effectiveness of the proposed model is demonstrated by amounts of experiments. Experimental results show that the mode collapse issue can be effectively alleviated by the introduced one-bit stroke encoding and few-shot semi-supervised training scheme, and that the proposed model outperforms the state-of-the-art models in fourteen font generation tasks in terms of four important evaluation metrics and the quality of generated characters. Besides CycleGAN, we also show that the proposed idea can be adapted to other existing models to improve their performance. The effectiveness of the proposed model for the zero-shot traditional Chinese font generation is also evaluated in this paper.

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io

在许多分类模型中，数据被离散化以更好地估计其分布。现有的离散方法通常是针对最大化离散数据的判别能力的，同时忽略了分类中数据离散化的主要目标是改善概括性能。结果，数据往往会超出许多小型垃圾箱，因为数据没有离散化保留了最大判别信息。因此，我们提出了一个最大依赖性最差（MDMD）标准，该标准可最大程度地提高离散数据的判别信息和概括能力。更具体地说，最大依赖性标准可最大化离散数据和分类变量之间的统计依赖性，而最小差异标准则明确最大程度地减少了给定离散方案的训练数据与验证数据之间的JS差异。拟议的MDMD标准在技术上很有吸引力，但是很难可靠地估计属性的高阶联合分布和分类变量。因此，我们进一步提出了一个更实用的解决方案，最大值 - 差异 - 差异（MRMD）离散方案，其中每个属性通过同时最大化判别信息和离散数据的概括能力分别离散化。将提出的MRMD与45个机器学习基准数据集的Naive Bayes分类框架下的最新离散算法进行了比较。它大大优于大多数数据集上所有比较的方法。

深度学习取得了长足的进步，用于图像中的对象检测。对象检测的检测准确性和计算成本取决于图像的空间分辨率，这可能会受到相机和存储注意事项的约束。压缩通常是通过减少空间或幅度分辨率或有时两者都对性能的众所周知的影响来实现的。检测精度还取决于感兴趣的对象与摄像机的距离。我们的工作研究了空间和振幅分辨率以及对象距离对物体检测准确性和计算成本的影响。我们开发了Yolov5（ra-Yolo）的分辨率 - 自适应变体，该变体基于输入图像的空间分辨率，它在特征金字塔和检测头中变化。为了训练和评估这种新方法，我们通过结合TJU和Eurocity数据集的图像来创建具有不同空间和振幅分辨率的图像数据集，并通过应用空间调整和压缩来生成不同的分辨率。我们首先表明Ra-Yolo在各种空间分辨率上实现了检测准确性和推理时间之间的良好权衡。然后，我们使用拟议的RA-YOLO模型评估空间和振幅分辨率对物体检测准确性的影响。我们证明，导致最高检测精度的最佳空间分辨率取决于“耐受性”图像大小。我们进一步评估了对象到摄像机对检测准确性的影响，并表明较高的空间分辨率可实现更大的检测范围。这些结果为选择图像空间分辨率和压缩设置提供了重要的指南，这些分辨率和压缩设置基于可用的带宽，存储，所需的推理时间和/或所需的检测范围，在实际应用中。

最近的作品证明了过度参数化学习中的双重下降现象：随着模型参数的数量的增加，多余的风险具有$ \ mathsf {u} $ - 在开始时形状，然后在模型高度过度参数化时再次减少。尽管最近在不同的环境（例如线性模型，随机特征模型和内核方法）下进行了研究，但在理论上尚未完全理解这种现象。在本文中，我们考虑了由两种随机特征组成的双随机特征模型（DRFM），并研究DRFM在脊回归中实现的多余风险。我们计算高维框架下的多余风险的确切限制，在这种框架上，训练样本量，数据尺寸和随机特征的维度往往会成比例地无限。根据计算，我们证明DRFM的风险曲线可以表现出三重下降。然后，我们提供三重下降现象的解释，并讨论随机特征维度，正则化参数和信噪比比率如何控制DRFMS风险曲线的形状。最后，我们将研究扩展到多个随机功能模型（MRFM），并表明具有$ K $类型的随机功能的MRFM可能会显示出$（K+1）$ - 折叠。我们的分析指出，具有特定数量下降的风险曲线通常在基于特征的回归中存在。另一个有趣的发现是，当学习神经网络在“神经切线内核”制度中时，我们的结果可以恢复文献中报告的风险峰值位置。

由于深度神经网络的开发，尤其是对于最近开发的无监督的JND代模型，对公正的显着差异（JND）建模做出了重大改进。但是，他们有一个主要的缺点，即在现实世界信号域而不是在人脑中的感知结构域中评估了生成的JND。当在这两个域中评估JND时，存在明显的差异，因为在现实世界中的视觉信号在通过人类视觉系统（HVS）传递到大脑之前已编码。因此，我们提出了一个受HVS启发的信号降解网络进行JND估计。为了实现这一目标，我们仔细分析了JND主观观察中的HVS感知过程，以获得相关的见解，然后设计受HVS启发的信号降解（HVS-SD）网络，以表示HVS中的信号降解。一方面，知识渊博的HVS-SD使我们能够评估感知域中的JND。另一方面，它提供了更准确的先验信息，以更好地指导JND生成。此外，考虑到合理的JND不应导致视觉注意力转移的要求，提出了视觉注意力丧失以控制JND的生成。实验结果表明，所提出的方法实现了SOTA性能，以准确估计HVS的冗余性。源代码将在https://github.com/jianjin008/hvs-sd-jnd上找到。