事实证明，视觉变压器（VIT）是有效的，可以通过大规模图像数据集训练2D图像理解任务；同时，作为一条单独的曲目，在对3D视觉世界进行建模时，例如体素或点云。但是，随着希望变压器能够成为异质数据的“通用”建模工具的越来越希望，到目前为止，用于2D和3D任务的VIT已经采用了截然不同的架构设计，这些设计几乎是不可传输的。这引起了一个雄心勃勃的问题：我们可以缩小2D和3D VIT体系结构之间的差距吗？作为一项试点研究，本文证明了使用标准的2D VIT体系结构了解3D视觉世界的有吸引力的承诺，仅在输入和输出水平上只有最小的定制而不会重新设计管道。为了从其2D兄弟姐妹构建3D VIT，我们将贴片嵌入和令牌序列“充气”，并配有旨在匹配3D数据几何形状的新位置编码机制。与高度自定义的3D特定设计相比，所得的“极简主义” 3D VIT（名为Simple3D Former）在流行的3D任务（例如对象分类，点云分割和室内场景检测）上表现出色。因此，它可以作为新3D VIT的强大基准。此外，我们注意到，除了科学的好奇心外，追求统一的2D-3D VIT设计具有实际相关性。具体而言，我们证明了Simple3D Former自然能够从大规模逼真的2D图像（例如Imagenet）中利用预先训练的重量的财富，可以插入以增强“免费” 3D任务性能。

有效地保留和编码结构功能从不规则和稀疏点点中的对象中的对象是对点云上3D对象检测的关键挑战。最近，变形金刚在许多2D甚至3D视觉任务上都表现出了有希望的表现。与固定和刚性卷积内核相比，变压器中的自发机制可以适应地排除无关或嘈杂点，因此适合保留不规则的LIDAR点云中的局部空间结构。但是，Transformer仅根据自我发项机制对点特征执行简单的总和，所有点具有相同的价值变换。这种各向同性操作缺乏捕获面向方向距离的局部结构的能力，这对于3D对象检测很重要。在这项工作中，我们提出了一个结构插入变压器（Seformer），它不仅可以将本地结构保存为传统变压器，而且还可以编码本地结构。与传统变压器中的自我发挥机制相比，Seformer基于与查询点的相对方向和距离学习了价值点的不同特征变换。然后，我们提出了一个基于Seformer的网络，用于高性能3D对象检测。广泛的实验表明，所提出的体系结构可以在Waymo Open Datatet上实现SOTA结果，这是自动驾驶的最大3D检测基准。具体而言，Seformer获得79.02％的地图，比现有作品高1.2％。我们将发布代码。

人工智能（AI）已被广泛应用于药物发现中，其主要任务是分子财产预测。尽管分子表示学习中AI技术的繁荣，但尚未仔细检查分子性质预测的一些关键方面。在这项研究中，我们对三个代表性模型，即随机森林，莫尔伯特和格罗弗进行了系统比较，该模型分别利用了三个主要的分子表示，扩展连接的指纹，微笑的字符串和分子图。值得注意的是，莫尔伯特（Molbert）和格罗弗（Grover）以自我监督的方式在大规模的无标记分子库中进行了预定。除了常用的分子基准数据集外，我们还组装了一套与阿片类药物相关的数据集进行下游预测评估。我们首先对标签分布和结构分析进行了数据集分析；我们还检查了阿片类药物相关数据集中的活动悬崖问题。然后，我们培训了4,320个预测模型，并评估了学习表示的有用性。此外，我们通过研究统计测试，评估指标和任务设置的效果来探索模型评估。最后，我们将化学空间的概括分解为施加间和支柱内的概括，并测量了预测性能，以评估两种设置下模型的普遍性。通过采取这种喘息，我们反映了分子财产预测的基本关键方面，希望在该领域带来更好的AI技术的意识。

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.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.

In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedback under a query budget. Due to the limited feedback information, existing query-based black-box attack methods often require many queries for attacking each benign example. To reduce query cost, we propose to utilize the feedback information across historical attacks, dubbed example-level adversarial transferability. Specifically, by treating the attack on each benign example as one task, we develop a meta-learning framework by training a meta-generator to produce perturbations conditioned on benign examples. When attacking a new benign example, the meta generator can be quickly fine-tuned based on the feedback information of the new task as well as a few historical attacks to produce effective perturbations. Moreover, since the meta-train procedure consumes many queries to learn a generalizable generator, we utilize model-level adversarial transferability to train the meta-generator on a white-box surrogate model, then transfer it to help the attack against the target model. The proposed framework with the two types of adversarial transferability can be naturally combined with any off-the-shelf query-based attack methods to boost their performance, which is verified by extensive experiments.

Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural networks have achieved competitive performance, the resulting policies are often over-parameterized black boxes that are difficult to interpret and deploy efficiently. More recent symbolic RL frameworks have shown that high-level domain-specific programming logic can be designed to handle both policy learning and symbolic planning. However, these approaches rely on coded primitives with little feature learning, and when applied to high-dimensional visual scenes, they can suffer from scalability issues and perform poorly when images have complex object interactions. To address these challenges, we propose \textit{Differentiable Symbolic Expression Search} (DiffSES), a novel symbolic learning approach that discovers discrete symbolic policies using partially differentiable optimization. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions, while also incorporating the strengths of neural networks for feature learning and optimization. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more and scalable than state-of-the-art symbolic RL methods, with a reduced amount of symbolic prior knowledge.