In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Specifically, we leverage a voxel-based neural implicit surface representation to encode and optimize the scene inside each voxel. Furthermore, we adopt an octree-based structure to divide the scene and support dynamic expansion, enabling our system to track and map arbitrary scenes without knowing the environment like in previous works. Moreover, we proposed a high-performance multi-process framework to speed up the method, thus supporting some applications that require real-time performance. The evaluation results show that our methods can achieve better accuracy and completeness than previous methods. We also show that our Vox-Fusion can be used in augmented reality and virtual reality applications. Our source code is publicly available at https://github.com/zju3dv/Vox-Fusion.

虚拟内容创建和互动在现代3D应用中起着重要作用，例如AR和VR。从真实场景中恢复详细的3D模型可以显着扩大其应用程序的范围，并在计算机视觉和计算机图形社区中进行了数十年的研究。我们提出了基于体素的隐式表面表示Vox-Surf。我们的Vox-Surf将空间分为有限的体素。每个体素将几何形状和外观信息存储在其角顶点。 Vox-Surf得益于从体素表示继承的稀疏性，几乎适用于任何情况，并且可以轻松地从多个视图图像中训练。我们利用渐进式训练程序逐渐提取重要体素，以进一步优化，以便仅保留有效的体素，从而大大减少了采样点的数量并增加了渲染速度。细素还可以视为碰撞检测的边界量。该实验表明，与其他方法相比，Vox-Surf表示可以学习精致的表面细节和准确的颜色，并以更少的记忆力和更快的渲染速度来学习。我们还表明，Vox-Surf在场景编辑和AR应用中可能更实用。

随着对深度学习民主化的向往，在资源约束设备上实施基于变压器的自然语言处理（NLP）模型的需求越来越大，以实施低延迟和高准确性。现有的BERT修剪方法要求域专家启发手工制作超参数，以在模型大小，延迟和准确性之间取得平衡。在这项工作中，我们提出了AE-Bert，这是一种具有有效评估的自动和高效的BERT修剪框架，以选择“良好”子网络候选（高精度），鉴于整体修剪比率的约束。我们提出的方法不需要人类专家的经验，并且可以在许多NLP任务上取得更好的准确性能。我们关于一般语言理解评估（胶水）基准的实验结果表明，AE-Bert优于Bert $ _ {\ Mathrm {base}} $的最先进的（SOTA）手工制作的修剪方法。在QNLI和RTE上，我们获得75 \％和42.8％的总体修剪比，同时获得更高的精度。在MRPC上，我们的得分比SOTA高4.6，在相同的整体修剪比为0.5。在STS-B上，与SOTA手工制作的修剪方法相比，我们可以达到40 \％的修剪比，而Spearman相关性的损失非常小。实验结果还表明，在模型压缩之后，单个bert $ _ {\ mathrm {base}} $ coder的推理时间在xilinx alveo u200 fpga板上具有1.83 $ \ times $ speedup，与intel（r）xeon相比）Gold 5218（2.30GHz）CPU，它显示了部署BERT $ _ {\ MATHRM {base}} $模型在计算限制设备上生成的方法生成的子网的合理性。

聚类是一项基本的机器学习任务，在文献中已广泛研究。经典聚类方法遵循以下假设：数据通过各种表示的学习技术表示为矢量化形式的特征。随着数据变得越来越复杂和复杂，浅（传统）聚类方法无法再处理高维数据类型。随着深度学习的巨大成功，尤其是深度无监督的学习，在过去的十年中，已经提出了许多具有深层建筑的代表性学习技术。最近，已经提出了深层聚类的概念，即共同优化表示的学习和聚类，因此引起了社区的日益关注。深度学习在聚类中的巨大成功，最基本的机器学习任务之一以及该方向的最新进展的巨大成功所激发。 - 艺术方法。我们总结了深度聚类的基本组成部分，并通过设计深度表示学习和聚类之间的交互方式对现有方法进行了分类。此外，该调查还提供了流行的基准数据集，评估指标和开源实现，以清楚地说明各种实验设置。最后但并非最不重要的一点是，我们讨论了深度聚类的实际应用，并提出了应有的挑战性主题，应将进一步的研究作为未来的方向。

深度散列在大规模图像检索中显示了有希望的性能。然而，由\ textBF {d} EEP \ TextBF {n} EETURT \ TextBF {n} etwork（DNN）提取的潜在代码将在二值化过程中不可避免地丢失语义信息，这损害了检索效率并使其充满挑战。虽然许多现有方法进行正规化以缓解量化错误，但我们弄清楚了度量和量化损耗之间的不兼容冲突。公制损失惩罚了阶级距离，以推动远处的不受约束的不同类别。更糟糕的是，它倾向于映射潜在的代码偏离理想的二值化点，并在二值化过程中产生严重的模糊性。基于二进制线性代码的最小距离，提出了提出基于二进制线性代码的最小距离，\ textbf {h}灰色引导\ textbf {h} Inge \ textbf {f}发射（hhf）以避免这种冲突。详细说明，我们仔细设计了一个特定的拐点，依赖于散列长度和类别号来平衡度量学习和量化学习。这种修改可防止网络落入深度散列中的局部度量最佳最小值。在CiFAR-10，CIFAR-100，ImageNet和MS-Coco中的广泛实验表明，HHF始终如一地优于现有技术，并且将其移植到其他方法中是坚固且柔韧的。

3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert curve-based cross-dimensionality distillation approach that facilitates the knowledge of 3D networks to improve the performance of 2D networks. The proposed Hilbert Distillation (HD) method preserves the structural information via the Hilbert curve, which maps high-dimensional (>=2) representations to one-dimensional continuous space-filling curves. Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations. We further propose a Variable-length Hilbert Distillation (VHD) method to dynamically shorten the walking stride of the Hilbert curve in activation feature areas and lengthen the stride in context feature areas, forcing the 2D networks to pay more attention to learning from activation features. The proposed algorithm outperforms the current state-of-the-art distillation techniques adapted to cross-dimensionality distillation on two classification tasks. Moreover, the distilled 2D networks by the proposed method achieve competitive performance with the original 3D networks, indicating the lightweight distilled 2D networks could potentially be the substitution of cumbersome 3D networks in the real-world scenario.

Change detection (CD) is an essential earth observation technique. It captures the dynamic information of land objects. With the rise of deep learning, convolutional neural networks (CNN) have shown great potential in CD. However, current CNN models introduce backbone architectures that lose detailed information during learning. Moreover, current CNN models are heavy in parameters, which prevents their deployment on edge devices such as UAVs. In this work, we tackle this issue by proposing RDP-Net: a region detail preserving network for CD. We propose an efficient training strategy that constructs the training tasks during the warmup period of CNN training and lets the CNN learn from easy to hard. The training strategy enables CNN to learn more powerful features with fewer FLOPs and achieve better performance. Next, we propose an effective edge loss that increases the penalty for errors on details and improves the network's attention to details such as boundary regions and small areas. Furthermore, we provide a CNN model with a brand new backbone that achieves the state-of-the-art empirical performance in CD with only 1.70M parameters. We hope our RDP-Net would benefit the practical CD applications on compact devices and could inspire more people to bring change detection to a new level with the efficient training strategy. The code and models are publicly available at https://github.com/Chnja/RDPNet.

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.