深度学习已成为火星探索的强大工具。火星地形细分是一项重要的火星愿景任务，它是漫游者自动计划和安全驾驶的基础。但是，现有的基于深度学习的地形细分方法遇到了两个问题：一个是缺乏足够的详细和高信心注释，另一个是模型过度依赖于注释的培训数据。在本文中，我们从联合数据和方法设计的角度解决了这两个问题。我们首先提出了一个新的火星地形细分数据集，该数据集包含6K高分辨率图像，并根据置信度稀疏注释，以确保标签的高质量。然后从这些稀疏的数据中学习，我们为火星地形细分的基于表示的学习框架，包括一个自我监督的学习阶段（用于预训练）和半监督的学习阶段（用于微调）。具体而言，对于自我监督的学习，我们设计了一个基于掩盖图像建模（MIM）概念的多任务机制，以强调图像的纹理信息。对于半监督的学习，由于我们的数据集很少注释，因此我们鼓励该模型通过在线生成和利用伪标签来挖掘每个图像中未标记的区域的信息。我们将数据集和方法命名为MARS（S $^{5} $ MARS）的自我监督和半监督分割。实验结果表明，我们的方法可以超越最先进的方法，并通过很大的边距提高地形分割性能。

视觉变压器（VIT）在视觉识别方面达到了最先进的性能，而变体的本地视觉变压器则取得了进一步的改进。本地视觉变压器的主要组成部分，本地注意力，在小型本地窗户上分别表现出注意力。我们将局部关注重新为渠道，以渠道局部连接，并从两个网络正规化的方式分析它，稀疏的连接性和权重共享以及重量计算。稀疏连接：跨通道没有连接，每个位置都连接到一个小型本地窗口中的位置。重量共享：一个位置的连接权重跨通道或每个频道内共享。动态重量：根据每个图像实例，可以动态预测连接权重。我们指出，本地注意力类似于深度卷积及其在稀疏连接中的动态版本。主要区别在于重量共享 - 深度卷积分享连接权重（内核权重）跨空间位置。我们从经验上观察到，基于深度卷积和具有较低计算复杂性的动态变体的模型与Swin Transformer（局部视觉变压器的实例），用于ImagEnet分类，可可对象检测和ADE Sentical，具有较低的计算复杂性的动态变体。分割。这些观察结果表明，本地视觉变压器利用两种正则化形式和动态重量来增加网络容量。代码可在https://github.com/atten4vis/demystifylocalvit上找到。

在本文中，我们从功能学习的角度解决了点云完成的具有挑战性的问题。我们的主要观察结果是，要恢复基础结构以及表面细节，给定部分输入，基本组件是一个很好的特征表示，可以同时捕获全球结构和局部几何细节。因此，我们首先提出了FSNET，这是一个功能结构模块，可以通过从本地区域学习多个潜在图案来适应汇总点的点功能。然后，我们将FSNET集成到粗线管道中，以完成点云完成。具体而言，采用2D卷积神经网络将特征图从FSNET解码为粗且完整的点云。接下来，使用一个点云UP抽样网络来从部分输入和粗糙的中间输出中生成密集的点云。为了有效利用局部结构并增强点分布均匀性，我们提出了IFNET，该点具有自校正机制的点提升模块，该模块可以逐步完善生成的密集点云的细节。我们已经在Shapenet，MVP和Kitti数据集上进行了定性和定量实验，这些实验表明我们的方法优于最先进的点云完成方法。

我们研究了视觉变压器的培训，用于半监督图像分类。变形金刚最近在众多监督的学习任务中表现出令人印象深刻的表现。令人惊讶的是，我们发现视觉变形金刚在半监督的想象中心设置上表现不佳。相比之下，卷积神经网络（CNNS）实现了小标记数据制度的卓越结果。进一步调查揭示了原因是CNN具有强大的空间归纳偏差。灵感来自这一观察，我们介绍了一个联合半监督学习框架，半统一，其中包含变压器分支，卷积分支和精心设计的融合模块，用于分支之间的知识共享。卷积分支在有限监督数据上培训，并生成伪标签，以监督变压器分支对未标记数据的培训。关于Imagenet的广泛实验表明，半统一达到75.5 \％的前1个精度，优于最先进的。此外，我们显示Semifirmer是一般框架，与大多数现代变压器和卷积神经结构兼容。

Videos are multimodal in nature. Conventional video recognition pipelines typically fuse multimodal features for improved performance. However, this is not only computationally expensive but also neglects the fact that different videos rely on different modalities for predictions. This paper introduces Hierarchical and Conditional Modality Selection (HCMS), a simple yet efficient multimodal learning framework for efficient video recognition. HCMS operates on a low-cost modality, i.e., audio clues, by default, and dynamically decides on-the-fly whether to use computationally-expensive modalities, including appearance and motion clues, on a per-input basis. This is achieved by the collaboration of three LSTMs that are organized in a hierarchical manner. In particular, LSTMs that operate on high-cost modalities contain a gating module, which takes as inputs lower-level features and historical information to adaptively determine whether to activate its corresponding modality; otherwise it simply reuses historical information. We conduct extensive experiments on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate the proposed approach can effectively explore multimodal information for improved classification performance while requiring much less computation.

评估防御模型的稳健性是对抗对抗鲁棒性研究的具有挑战性的任务。僵化的渐变，先前已经发现了一种梯度掩蔽，以许多防御方法存在并导致鲁棒性的错误信号。在本文中，我们确定了一种更细微的情况，称为不平衡梯度，也可能导致过高的对抗性鲁棒性。当边缘损耗的一个术语的梯度主导并将攻击朝向次优化方向推动时，发生不平衡梯度的现象。为了利用不平衡的梯度，我们制定了分解利润率损失的边缘分解（MD）攻击，并通过两阶段过程分别探讨了这些术语的攻击性。我们还提出了一个Multared和Ensemble版本的MD攻击。通过调查自2018年以来提出的17个防御模型，我们发现6种型号易受不平衡梯度的影响，我们的MD攻击可以减少由最佳基线独立攻击评估的鲁棒性另外2％。我们还提供了对不平衡梯度的可能原因和有效对策的深入分析。

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.