地球表面不断变化，识别变化在城市规划和可持续发展中发挥着重要作用。虽然多年来已经成功开发了变化检测技术，但这些技术仍然仅限于相关领域的专家和促进者。为了为每个用户提供灵活的进入更改信息并帮助他们更好地了解陆地覆盖的变化，我们介绍了一种新的任务：在多时间空中图像上更改基于检测的视觉问题应答（CDVQA）。特别地，可以查询多时间图像以根据两个输入图像之间的内容改变获得基于高电平的改变的信息。我们首先使用自动问题答案生成方法构建CDVQA数据集，包括多时间图像问题答案三联网。然后，在这项工作中设计了一个基线CDVQA框架，它包含四个部分：多时间特征编码，多时间融合，多模态融合和答案预测。此外，我们还将更改增强模块引入多时间特征编码，旨在结合更多的变更相关信息。最后，研究了CDVQA任务的性能研究不同骨干和多时间融合策略的影响。实验结果为开发更好的CDVQA模型提供了有用的见解，这对未来对此任务的研究很重要。我们将通过公开提供我们的数据集和代码。

用于图像分类的最可公开的数据集是单个标签，而图像在我们的日常生活中是固有的多标记。这种注释差距使得许多预先接受的单标准分类模型在实际情况下失败。该注释问题更加关注空中图像：从传感器收集的空中数据自然地覆盖具有多个标签的相对大的陆地面积，而被广泛可用的注释空中数据集（例如，UCM，AID）是单标记的。作为手动注释的多标签空中图像将是时间/劳动，我们提出了一种新的自我校正综合域适应（SCIDA）方法，用于自动多标签学习。 SCIDA是弱监督，即，自动学习多标签图像分类模型，从使用大量的公共可用的单一标签图像。为实现这一目标，我们提出了一种新颖的标签 - 明智的自我校正（LWC）模块，以更好地探索潜在的标签相关性。该模块还使无监督的域适配（UDA）从单个到多标签数据中可能。对于模型培训，所提出的型号仅使用单一标签信息，但不需要先验知识的多标记数据;它预测了多标签空中图像的标签。在我们的实验中，用单标签的MAI-AID-S和MAI-UCM-S数据集接受培训，所提出的模型直接在收集的多场景空中图像（MAI）数据集上进行测试。

Semantic segmentation of UAV aerial remote sensing images provides a more efficient and convenient surveying and mapping method for traditional surveying and mapping. In order to make the model lightweight and improve a certain accuracy, this research developed a new lightweight and efficient network for the extraction of ground features from UAV aerial remote sensing images, called LDMCNet. Meanwhile, this research develops a powerful lightweight backbone network for the proposed semantic segmentation model. It is called LDCNet, and it is hoped that it can become the backbone network of a new generation of lightweight semantic segmentation algorithms. The proposed model uses dual multi-scale context modules, namely the Atrous Space Pyramid Pooling module (ASPP) and the Object Context Representation module (OCR). In addition, this research constructs a private dataset for semantic segmentation of aerial remote sensing images from drones. This data set contains 2431 training sets, 945 validation sets, and 475 test sets. The proposed model performs well on this dataset, with only 1.4M parameters and 5.48G floating-point operations (FLOPs), achieving an average intersection-over-union ratio (mIoU) of 71.12%. 7.88% higher than the baseline model. In order to verify the effectiveness of the proposed model, training on the public datasets "LoveDA" and "CITY-OSM" also achieved excellent results, achieving mIoU of 65.27% and 74.39%, respectively.

基于深度神经网络的时间序列分类方法很容易在UCR数据集上过度拟合，这是由这些数据集的几次拍摄问题引起的。因此，为了减轻进一步提高准确性的过度拟合现象，我们首先提出标记为IncepionTime（LSTIME）的标记平滑，这与软标签的信息相比，与硬标签相比。接下来，提出了通过LSTIME手动调整软标签，提出了成立时间（KDTIME）的知识蒸馏，以便通过教师模型自动生成软标签。最后，为了纠正来自教师模型的错误预测的软标签，提出了具有成立时间（KDCTIME）的校准的知识蒸馏，在其中包含两个可选的校准策略，即通过重新排序（KDCR）通过翻译（KDCT）和KDC的可选校准策略（KDC）（KDCR ）。实验结果表明，KDCTIME的准确性很有前景，而其推理时间比火箭速度快两个数量级，具有可接受的训练时间开销。

多种子体形成以及障碍物避免是多助理系统领域最受研究的主题之一。虽然一些经典控制器等模型预测控制（MPC）和模糊控制实现了一定的成功措施，但大多数都需要在恶劣环境中无法访问的精确全局信息。另一方面，一些基于加强学习（RL）的方法采用了领导者 - 跟随器结构来组织不同的代理行为，这使得造成诸如机动性和鲁棒性的瓶颈之间的代理之间的合作。在本文中，我们提出了一种基于多功能钢筋学习（Marl）的分布式形成和障碍避免方法。我们系统中的代理只能利用本地和相关信息来分发决策和控制自己。在多代理系统中的代理将在任何断开连接的情况下快速重新组织到新的拓扑中。与基线（经典控制方法和其他基于RL的方法）相比，我们的方法实现了更好的形成误差，形成收敛速度和障碍物的成功率的成功率。通过使用Ackermann-tenting车辆的模拟和硬件实现来验证我们的方法的可行性。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

To generate high quality rendering images for real time applications, it is often to trace only a few samples-per-pixel (spp) at a lower resolution and then supersample to the high resolution. Based on the observation that the rendered pixels at a low resolution are typically highly aliased, we present a novel method for neural supersampling based on ray tracing 1/4-spp samples at the high resolution. Our key insight is that the ray-traced samples at the target resolution are accurate and reliable, which makes the supersampling an interpolation problem. We present a mask-reinforced neural network to reconstruct and interpolate high-quality image sequences. First, a novel temporal accumulation network is introduced to compute the correlation between current and previous features to significantly improve their temporal stability. Then a reconstruct network based on a multi-scale U-Net with skip connections is adopted for reconstruction and generation of the desired high-resolution image. Experimental results and comparisons have shown that our proposed method can generate higher quality results of supersampling, without increasing the total number of ray-tracing samples, over current state-of-the-art methods.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying background distribution in the entire scene or the transmittance over the ray dimension, limiting their performance on static and occlusion areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance fields offers high-quality view synthesis and a 3D solution to $\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene, which is called $\text{D}^4$NeRF. Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively. Each ray sample is given an additional occlusion weight to indicate the transmittance lying in the static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic scenes and our urban driving scenes acquired from an autonomous-driving dataset. Extensive experiments demonstrate that our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background. Our code will be released at https://github.com/Luciferbobo/D4NeRF.

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.