Video Anomaly Detection (VAD), aiming to identify abnormalities within a
specific context and timeframe, is crucial for intelligent Video Surveillance
Systems. While recent deep learning-based VAD models have shown promising
results by generating high-resolution frames, they often lack competence in
preserving detailed spatial and temporal coherence in video frames. To tackle
this issue, we propose a self-supervised learning approach for VAD through an
inter-patch relationship prediction task. Specifically, we introduce a
two-branch vision transformer network designed to capture deep visual features
of video frames, addressing spatial and temporal dimensions responsible for
modeling appearance and motion patterns, respectively. The inter-patch
relationship in each dimension is decoupled into inter-patch similarity and the
order information of each patch. To mitigate memory consumption, we convert the
order information prediction task into a multi-label learning problem, and the
inter-patch similarity prediction task into a distance matrix regression
problem. Comprehensive experiments demonstrate the effectiveness of our method,
surpassing pixel-generation-based methods by a significant margin across three
public benchmarks. Additionally, our approach outperforms other self-supervised
learning-based methods.

通过自监督学习和视觉变换网络，提出一种用于视频异常检测的方法，能够有效地保留视频帧的空间和时间上的一致性，并且在三个公共基准测试中表现优于像素生成方法和其他自监督学习方法。

视频异常检测中的补丁时空关系预测

Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection

We introduce a shape-sensitive loss function for catheter and guidewire
segmentation and utilize it in a vision transformer network to establish a new
state-of-the-art result on a large-scale X-ray images dataset. We transform
network-derived predictions and their corresponding ground truths into signed
distance maps, thereby enabling any networks to concentrate on the essential
boundaries rather than merely the overall contours. These SDMs are subjected to
the vision transformer, efficiently producing high-dimensional feature vectors
encapsulating critical image attributes. By computing the cosine similarity
between these feature vectors, we gain a nuanced understanding of image
similarity that goes beyond the limitations of traditional overlap-based
measures. The advantages of our approach are manifold, ranging from scale and
translation invariance to superior detection of subtle differences, thus
ensuring precise localization and delineation of the medical instruments within
the images. Comprehensive quantitative and qualitative analyses substantiate
the significant enhancement in performance over existing baselines,
demonstrating the promise held by our new shape-sensitive loss function for
improving catheter and guidewire segmentation.

我们引入了一种对导管和导丝进行形状敏感的损失函数，并将其应用于视觉转换网络中，在大规模 X 射线图像数据集上取得了最新的研究成果。我们将网络预测和相应的真值转换为有符号距离图，从而使任何网络能够集中精力处理关键的边界而不仅限于整体轮廓。通过计算这些特征向量之间的余弦相似度，我们对图像相似性有了更细致的理解，超越了传统基于重叠度的测量方法的局限。我们的方法具有许多优势，包括尺度和平移不变性，以及对微小差异的较好检测，从而确保图像中医疗仪器的准确定位和描述。全面的定量和定性分析证实了相比现有基准的显著性能提升，展示了我们新的形状敏感损失函数在改善导管和导丝分割方面的潜力。

针对导管和导丝分割的形状敏感损失

Shape-Sensitive Loss for Catheter and Guidewire Segmentation

Robotic technology has been widely used in nowadays society, which has made
great progress in various fields such as agriculture, manufacturing and
entertainment. In this paper, we focus on the topic of drumming robots in
entertainment. To this end, we introduce an improving drumming robot that can
automatically complete music transcription based on the popular vision
transformer network based on the attention mechanism. Equipped with the
attention transformer network, our method can efficiently handle the sequential
audio embedding input and model their global long-range dependencies. Massive
experimental results demonstrate that the improving algorithm can help the
drumming robot promote drum classification performance, which can also help the
robot to enjoy a variety of smart applications and services.

该论文介绍了一种改进的敲鼓机器人，它能够基于注意力机制和流行视觉转换网络，自动完成音乐转录，并能有效处理音频嵌入输入的全局长程依赖关系，实验证明改进算法提高了机器人的敲鼓分类性能，为机器人提供了各种智能应用和服务。