多标签图像分类是计算机愿景中的基本但具有挑战性的任务。在过去的几十年里，解决方案探索语义标签之间的关系取得了很大进展。然而，标签的潜在空间上下文信息被剥削。为了解决这个问题，提出了一种空间背景感知的深神经网络，以考虑语义和空间信息的考虑标签。在Microsoft Coco和Pascal VOC上评估了这一提议的框架，用于图像多标签的两个广泛使用的基准数据集。结果表明，该方法优于处理多标签图像分类问题的最先进解决方案。

分割是图像处理的基本操作。卷积操作遭受有限的接收领域，而全球建模是对分段任务的基础。在本文中，我们将图形卷积应用于分割任务，并提出改进的\ Texit {Laplacian}。与现有方法不同，我们的\ Textit {Laplacian}是数据相关的，我们介绍了两个注意力对角线矩阵来学习更好的顶点关系。另外，在执行基于图形的信息传播时，它利用了区域和边界信息。具体地，我们通过学习图表表示的关于不同类的边界意识区域 - 明智相关的模型和原因，其能够操纵沿着物体边界的空间增强的各个区域的长距离语义推理。我们的模型非常适合获得全局语义区域信息，同时也可以同时容纳局部空间边界特征。两种挑战数据集的实验表明，我们的方法优于最先进的方法在结肠镜检查中的息肉中的息肉和光盘和光学杯中的光盘和光学杯在彩色眼底图像上的分割。

本文涉及低级矩阵恢复问题的$ \ ell_ {2,0} $ \ ell_ {2,0} $ - 正则化分解模型及其计算。引入了Qual $ \ ell_ {2,0} $ - 因子矩阵的规范，以促进因素和低级别解决方案的柱稀疏性。对于这种不透露的不连续优化问题，我们开发了一种具有外推的交替的多种化 - 最小化（AMM）方法，以及一个混合AMM，其中提出了一种主要的交替的近端方法，以寻找与较少的非零列和带外推的AMM的初始因子对。然后用于最小化平滑的非凸损失。我们为所提出的AMM方法提供全局收敛性分析，并使用非均匀采样方案将它们应用于矩阵完成问题。数值实验是用综合性和实际数据示例进行的，并且与核形态正则化分解模型的比较结果和MAX-NORM正则化凸模型显示柱$ \ ell_ {2,0} $ - 正则化分解模型具有优势在更短的时间内提供较低误差和排名的解决方案。

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

Video-language pre-training has advanced the performance of various downstream video-language tasks. However, most previous methods directly inherit or adapt typical image-language pre-training paradigms to video-language pre-training, thus not fully exploiting the unique characteristic of video, i.e., temporal. In this paper, we propose a Hierarchical Temporal-Aware video-language pre-training framework, HiTeA, with two novel pre-training tasks for modeling cross-modal alignment between moments and texts as well as the temporal relations of video-text pairs. Specifically, we propose a cross-modal moment exploration task to explore moments in videos, which results in detailed video moment representation. Besides, the inherent temporal relations are captured by aligning video-text pairs as a whole in different time resolutions with multi-modal temporal relation exploration task. Furthermore, we introduce the shuffling test to evaluate the temporal reliance of datasets and video-language pre-training models. We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e.g., SSv2-Template and SSv2-Label) with 8.6% and 11.1% improvement respectively. HiTeA also demonstrates strong generalization ability when directly transferred to downstream tasks in a zero-shot manner. Models and demo will be available on ModelScope.

Face manipulation detection has been receiving a lot of attention for the reliability and security of the face images. Recent studies focus on using auxiliary information or prior knowledge to capture robust manipulation traces, which are shown to be promising. As one of the important face features, the face depth map, which has shown to be effective in other areas such as the face recognition or face detection, is unfortunately paid little attention to in literature for detecting the manipulated face images. In this paper, we explore the possibility of incorporating the face depth map as auxiliary information to tackle the problem of face manipulation detection in real world applications. To this end, we first propose a Face Depth Map Transformer (FDMT) to estimate the face depth map patch by patch from a RGB face image, which is able to capture the local depth anomaly created due to manipulation. The estimated face depth map is then considered as auxiliary information to be integrated with the backbone features using a Multi-head Depth Attention (MDA) mechanism that is newly designed. Various experiments demonstrate the advantage of our proposed method for face manipulation detection.

Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning.

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.

We propose, Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in \cite{guo2022monte}, for solving general nonlocal models such as integral equations and nonlocal PDEs. Similar as in MC-fPINNs, our MC-Nonlocal-PINNs handle the nonlocal operators in a Monte Carlo way, resulting in a very stable approach for high dimensional problems. We present a variety of test problems, including high dimensional Volterra type integral equations, hypersingular integral equations and nonlocal PDEs, to demonstrate the effectiveness of our approach.

Marine waves significantly disturb the unmanned surface vehicle (USV) motion. An unmanned aerial vehicle (UAV) can hardly land on a USV that undergoes irregular motion. An oversized landing platform is usually necessary to guarantee the landing safety, which limits the number of UAVs that can be carried. We propose a landing system assisted by tether and robot manipulation. The system can land multiple UAVs without increasing the USV's size. An MPC controller stabilizes the end-effector and tracks the UAVs, and an adaptive estimator addresses the disturbance caused by the base motion. The working strategy of the system is designed to plan the motion of each device. We have validated the manipulator controller through simulations and well-controlled indoor experiments. During the field tests, the proposed system caught and placed the UAVs when the disturbed USV roll range was approximately 12 degrees.