Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. A coral reef ecosystem usually consists of reefs, each of which is like a tall building in any city. These reef-building corals secrete hard calcareous exoskeletons that give them structural rigidity, and are also a prerequisite for our accurate 3D modeling and semantic mapping using advanced photogrammetric computer vision and machine learning. Underwater videography as a modern underwater remote sensing tool is a high-resolution coral habitat survey and mapping technique. In this paper, detailed 3D mesh models, digital surface models and orthophotos of the coral habitat are generated from the collected coral images and underwater control points. Meanwhile, a novel pixel-wise semantic segmentation approach of orthophotos is performed by advanced deep learning. Finally, the semantic map is mapped into 3D space. For the first time, 3D fine-grained semantic modeling and rugosity evaluation of coral reefs have been completed at millimeter (mm) accuracy. This provides a new and powerful method for understanding the processes and characteristics of coral reef change at high spatial and temporal resolution under climate change.

它得到了很好的认识到，从深度感知的LIDAR点云和语义富有的立体图像中融合互补信息将有利于3D对象检测。然而，探索稀疏3D点和密集2D像素之间固有的不自然相互作用并不重要。为了简化这种困难，最近的建议通常将3D点投影到2D图像平面上以对图像数据进行采样，然后聚合点处的数据。然而，这种方法往往遭受点云和RGB图像的分辨率之间的不匹配，导致次优性能。具体地，作为多模态数据聚合位置的稀疏点导致高分辨率图像的严重信息丢失，这反过来破坏了多传感器融合的有效性。在本文中，我们呈现VPFNET - 一种新的架构，可以在“虚拟”点处巧妙地对齐和聚合点云和图像数据。特别地，它们的密度位于3D点和2D像素的密度之间，虚拟点可以很好地桥接两个传感器之间的分辨率间隙，从而保持更多信息以进行处理。此外，我们还研究了可以应用于点云和RGB图像的数据增强技术，因为数据增强对迄今为止对3D对象探测器的贡献不可忽略。我们对Kitti DataSet进行了广泛的实验，与最先进的方法相比，观察到了良好的性能。值得注意的是，我们的VPFNET在KITTI测试集上实现了83.21 \％中等3D AP和91.86 \％适度的BEV AP，自2021年5月21日起排名第一。网络设计也考虑了计算效率 - 我们可以实现FPS 15对单个NVIDIA RTX 2080TI GPU。该代码将用于复制和进一步调查。

在基于变压器的模型中通常观察到令牌均匀性，在经过变压器中经过堆叠的多个自我发场层后，不同的令牌共享大量相似信息。在本文中，我们建议使用每个变压器层的输出的奇异值的分布来表征令牌均匀性的现象，并从经验上说明，偏斜的奇异值分布可以减轻“令牌均匀性”问题。基于我们的观察结果，我们定义了奇异值分布的几种理想特性，并提出了一种新的转换函数，以更新奇异值。我们表明，除了减轻令牌均匀性外，转换功能还应保留原始嵌入空间中的当地邻域结构。我们提出的奇异价值变换函数应用于伯特，阿尔伯特，罗伯塔和德文尔特等一系列基于变压器的语言模型，并且在语义文本相似性评估和一系列胶水任务中观察到了改善的性能。我们的源代码可在https://github.com/hanqi-qi/tokenuni.git上找到。

我们提出了VDL-Surogate，这是一种基于视图的神经网络贴属替代模型，用于集合模拟的参数空间探索，该模拟允许高分辨率可视化和用户指定的视觉映射。支持替代物的参数空间探索允许域科学家预览模拟结果，而无需运行大量计算成本的模拟。但是，受计算资源的限制，现有的替代模型可能无法产生以可视化和分析的足够分辨率的预览。为了提高计算资源的有效利用并支持高分辨率探索，我们从不同的角度进行射线铸造以收集样品并产生紧凑的潜在表示。这种潜在的编码过程降低了替代模型培训的成本，同时保持产出质量。在模型训练阶段，我们选择观点以覆盖整个观看球体，并为所选观点提供相应的VDL-Surrogate模型。在模型推理阶段，我们在先前选择的观点上预测潜在表示，并将潜在表示形式解码为数据空间。对于任何给定的观点，我们在选定的观点上对解码数据进行插值，并使用用户指定的视觉映射生成可视化。我们展示了VDL-Surogate在宇宙学和海洋模拟中的有效性和效率，并具有定量和定性评估。源代码可在\ url {https://github.com/trainsn/vdl-surrogate}上公开获得。

近年来，人们对开发自然语言处理（NLP）中可解释模型的利益越来越多。大多数现有模型旨在识别输入功能，例如对于模型预测而言重要的单词或短语。然而，在NLP中开发的神经模型通常以层次结构的方式构成单词语义，文本分类需要层次建模来汇总本地信息，以便处理主题和标签更有效地转移。因此，单词或短语的解释不能忠实地解释文本分类中的模型决策。本文提出了一种新型的层次解释性神经文本分类器，称为提示，该分类器可以自动以层次结构方式以标记相关主题的形式生成模型预测的解释。模型解释不再处于单词级别，而是基于主题作为基本语义单元。评论数据集和新闻数据集的实验结果表明，我们所提出的方法与现有最新的文本分类器相当地达到文本分类结果，并比其他可解释的神经文本更忠实于模型的预测和更好地理解人类的解释分类器。

根据国家学院，每周速度，垂直结构和环流电流（LC）的持续时间及其漩涡的预测对于了解海洋学和生态系统，以及减轻墨西哥湾的人为和自然灾害的结果至关重要（GOM）。然而，这一预测是一个具有挑战性的问题，因为LC行为由多个时间尺度的远程空间连接主导。在本文中，我们扩展了时空预测学习，将其效力显示为超越视频预测，到4D模型，即用于3D地理空间预测的时间序列的新型物理知识的张力列车Convlstm（Pitt-convlstm）。具体而言，我们提出1）一种新的4D高阶经复制神经网络，具有经验正交函数分析，以捕获每个层次结构的隐藏不相关的模式，2）卷积的张力串分解，以捕获更高阶的时空相关性，3 ）通过向域专家提供从域专家提供的现有物理知识，以便在潜在空间中通知学习。我们提出的方法的优点是显而易见的：通过物理定律的限制，它同时学习每个时间框架内帧的依赖性（包括短期和长期的高层次的依赖）和跨层级关系良好的表示。从GOM收集的地理空间数据的实验表明，PITT-COMMLSTM在预测LC的体积速度及其漩涡的时间内超过一周内的最先进的方法。

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.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.