无人机（UAV）跟踪在农业，导航和公共安全等中具有广泛的潜在应用。但是，计算资源，电池容量和无人机的最大负载的局限性阻碍了无人机上基于深度学习的跟踪算法的部署。因此，由于其高效率，歧视性相关过滤器（DCF）跟踪器在无人机跟踪社区中脱颖而出。但是，它们的精度通常比基于深度学习的跟踪器要低得多。模型压缩是一种有希望的方法，可以缩小基于DCF和深度学习的跟踪器之间差距（即效率，精度），这并没有引起无人机跟踪中的很多关注。在本文中，我们提出了P-SIAMFC ++跟踪器，该跟踪器是第一个使用基于等级的过滤器修剪来压缩SIAMFC ++模型的方法，在效率和精度之间取得了显着的平衡。我们的方法是一般的，可能会鼓励通过模型压缩对无人机跟踪的进一步研究。在四个无人机基准测试中进行的广泛实验，包括UAV123@10FPS，DTB70，UAVDT和Vistrone2018，表明P-SiAMFC ++跟踪器显着胜过最先进的无人机跟踪方法。

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate output scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on the small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. We retrospectively collected 595 cases between February 2018 and February 2022. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. We use the t-test for comparing quantitative results from all models and a radiologist. The proposed model achieves an average IoU of 0.867 and average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better (P<0.05) than two baseline models and not significantly different from a radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.

在多方转换方案中，重叠的语音检测（OSD）对于语音应用至关重要。尽管进行了许多研究工作和进展，与语音活动检测（VAD）相比，OSD仍然是一个开放的挑战，其总体表现远非令人满意。大多数先前的研究通常将OSD问题作为标准分类问题提出，以识别二进制（OSD）或三级标签（联合VAD和OSD）的语音。与主流相反，本研究从新的角度研究了联合VAD和OSD任务。特别是，我们建议使用多EXIT体系结构扩展传统的分类网络。这样的体系结构使我们的系统具有独特的功能，可以使用早期出口的低级功能或上次出口的高级功能来识别类。此外，采用了两种培训方案，知识蒸馏和密集的联系，以进一步提高我们的系统性能。基准数据集（AMI和DIHARD-III）的实验结果验证了我们提出的系统的有效性和通用性。我们的消融进一步揭示了拟议方案的互补贡献。在AMI上的$ F_1 $得分为0.792，而Dihard-III上的0.625分数，我们提出的系统在这些数据集上的表现优于几个顶级性能模型，但在两个数据集中也超过了当前的最新型号。除了性能收益外，我们提出的系统还为质量复杂性权衡提供了另一个吸引人的潜力，这是有效的OSD部署的高度优先。

为了解决单声道语音增强问题，已经进行了大量研究，以通过在语音混合物或时间域中学到的内域进行操作来增强语音，或者在时间域中 - 固定的全乐队短时间傅立叶的频率域变换（STFT）频谱图。最近，已经提出了一些关于基于子频段的语音增强的研究。通过通过子兰频谱图上的操作增强语音，这些研究表明了DNS2020基准数据集上的竞争性能。尽管有吸引力，但这个新的研究方向尚未得到充分探索，并且仍然有改进的余地。因此，在这项研究中，我们深入研究了最新的研究方向，并提出了一个基于子兰的语音增强系统，具有感知动机的优化和双重变换，称为PT-FSE。特别是，我们提出的PT-FSE模型通过三项努力改善了其主链（一种全频段和子融合模型）。首先，我们设计了一个旨在加强全局频率相关性的频率变换模块。然后引入时间转换以捕获远距离时间上下文。最后，提出了一种新的损失，具有人类听觉感知的性质杠杆作用，以促进该模型专注于低频增强。为了验证我们提出的模型的有效性，在DNS2020数据集上进行了广泛的实验。实验结果表明，我们的PT-FSE系统在其骨架上取得了重大改进，但也比当前的最新面积胜过，而比SOTA小27％。在基准数据集上，NB-PESQ平均为3.57，我们的系统提供了迄今报告的最佳语音增强结果。

近年来，基于注意力的场景文本识别方法非常受欢迎，并吸引了许多研究人员的兴趣。基于注意力的方法可以将注意力集中在解码过程中的小区域甚至单点上，其中注意矩阵几乎是一个旋转分布。此外，在推断过程中，所有注意力矩阵都将加权整个特征地图，从而导致巨大的冗余计算。在本文中，我们提出了一个用于场景文本识别的有效无注意的单点解码网络（称为SPDN），该网络可以取代传统的基于注意力的解码网络。具体而言，我们建议单点采样模块（SPSM）有效地在特征映射上为解码一个字符的一个关键点采样。这样，我们的方法不仅可以精确地找到每个字符的关键点，还可以删除冗余计算。基于SPSM，我们设计了一个高效且新颖的单点解码网络，以替代基于注意力的解码网络。对公开基准测试的广泛实验证明，我们的SPDN可以大大提高解码效率而不牺牲性能。

低光图像增强是某些复杂视觉任务的关键预处理任务。目标检测，图像分割和图像识别结果都受图像增强的影响直接影响。但是，当前使用的大多数图像增强技术不会产生令人满意的结果，并且这些增强的网络具有相对较弱的鲁棒性。我们建议使用U-NET作为其主要结构的改进网络，并将许多不同的注意机制作为解决问题的解决方案。在特定的应用程序中，我们将网络用作生成器和LSGAN作为培训框架，以获得更好的增强结果。我们证明了本文随后的实验中提出的网络Brightennet的有效性。它产生的结果既可以保留图像细节，又符合人类视觉标准。

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.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.