Social behavior, defined as the process by which individuals act and react in
response to others, is crucial for the function of societies and holds profound
implications for mental health. To fully grasp the intricacies of social
behavior and identify potential therapeutic targets for addressing social
deficits, it is essential to understand its core principles. Although machine
learning algorithms have made it easier to study specific aspects of complex
behavior, current methodologies tend to focus primarily on single-animal
behavior. In this study, we introduce LISBET (seLf-supervIsed Social BEhavioral
Transformer), a model designed to detect and segment social interactions. Our
model eliminates the need for feature selection and extensive human annotation
by using self-supervised learning to detect and quantify social behaviors from
dynamic body parts tracking data. LISBET can be used in hypothesis-driven mode
to automate behavior classification using supervised finetuning, and in
discovery-driven mode to segment social behavior motifs using unsupervised
learning. We found that motifs recognized using the discovery-driven approach
not only closely match the human annotations but also correlate with the
electrophysiological activity of dopaminergic neurons in the Ventral Tegmental
Area (VTA). We hope LISBET will help the community improve our understanding of
social behaviors and their neural underpinnings.

社交行为是社会功能和心理健康的关键，本研究介绍了 LISBET 模型，利用自监督学习从动态身体部位跟踪数据中检测和量化社交行为，并可用于行为分类和社交行为模式分割。研究显示，通过该方法得到的行为模式与人工注释高度一致，并与腹侧被盖区多巴胺神经元的电生理活动相关，有望提高对社交行为及其神经基础的理解。

LISBET：用于社交行为模式自动分割的自监督 Transformer 模型

LISBET: a self-supervised Transformer model for the automatic  segmentation of social behavior motifs

Effective classification of autonomous vehicle (AV) driving behavior emerges
as a critical area for diagnosing AV operation faults, enhancing autonomous
driving algorithms, and reducing accident rates. This paper presents the
Gramian Angular Field Vision Transformer (GAF-ViT) model, designed to analyze
AV driving behavior. The proposed GAF-ViT model consists of three key
components: GAF Transformer Module, Channel Attention Module, and Multi-Channel
ViT Module. These modules collectively convert representative sequences of
multivariate behavior into multi-channel images and employ image recognition
techniques for behavior classification. A channel attention mechanism is
applied to multi-channel images to discern the impact of various driving
behavior features. Experimental evaluation on the Waymo Open Dataset of
trajectories demonstrates that the proposed model achieves state-of-the-art
performance. Furthermore, an ablation study effectively substantiates the
efficacy of individual modules within the model.

本研究提出了 Gramian Angular Field Vision Transformer (GAF-ViT) 模型，用于分析自动驾驶汽车 (AV) 的驾驶行为。该模型通过将多变量行为的代表序列转化为多通道图像，并应用图像识别技术进行行为分类，实现了对多通道图像的特征驾驶行为的识别。对 Waymo Open 数据集的实验评估表明，该模型实现了最先进的性能，同时消融研究有效地证实了模型中各个组件的功效。

基于格里曼角度场视觉变换器的自动驾驶车辆驾驶行为探索

Exploring Driving Behavior for Autonomous Vehicles Based on Gramian  Angular Field Vision Transformer

Recently developed methods for video analysis, especially models for pose
estimation and behavior classification, are transforming behavioral
quantification to be more precise, scalable, and reproducible in fields such as
neuroscience and ethology. These tools overcome long-standing limitations of
manual scoring of video frames and traditional "center of mass" tracking
algorithms to enable video analysis at scale. The expansion of open-source
tools for video acquisition and analysis has led to new experimental approaches
to understand behavior. Here, we review currently available open-source tools
for video analysis and discuss how to set up these methods for labs new to
video recording. We also discuss best practices for developing and using video
analysis methods, including community-wide standards and critical needs for the
open sharing of datasets and code, more widespread comparisons of video
analysis methods, and better documentation for these methods especially for new
users. We encourage broader adoption and continued development of these tools,
which have tremendous potential for accelerating scientific progress in
understanding the brain and behavior.

本研究综述了最近发展的视频分析方法，特别是姿势估计和行为分类的模型，这些方法在神经科学和行为学等领域中使行为量化更加精确、可扩展和可重现。我们还讨论了使用这些方法的最佳实践，包括社区标准和数据集、代码的共享，以及新用户的更好文档说明。