Gesture Generation from Trimodal Context for Humanoid Robots

Natural co-speech gestures are essential components to improve the experience of Human-robot interaction (HRI). However, current gesture generation approaches have many limitations of not being natural, not aligning with the speech and content, or the lack of diverse speaker styles. Therefore, this work aims to repoduce the work by Yoon et,al generating natural gestures in simulation based on tri-modal inputs and apply this to a robot. During evaluation, ``motion variance'' and ``Frechet Gesture Distance (FGD)'' is employed to evaluate the performance objectively. Then, human participants were recruited to subjectively evaluate the gestures. Results show that the movements in that paper have been successfully transferred to the robot and the gestures have diverse styles and are correlated with the speech. Moreover, there is a significant likeability and style difference between different gestures.

HIC-YOLOv5: Improved YOLOv5 For Small Object Detection

Small object detection has been a challenging problem in the field of object detection. There has been some works that proposes improvements for this task, such as adding several attention blocks or changing the whole structure of feature fusion networks. However, the computation cost of these models is large, which makes deploying a real-time object detection system unfeasible, while leaving room for improvement. To this end, an improved YOLOv5 model: HIC-YOLOv5 is proposed to address the aforementioned problems. Firstly, an additional prediction head specific to small objects is added to provide a higher-resolution feature map for better prediction. Secondly, an involution block is adopted between the backbone and neck to increase channel information of the feature map. Moreover, an attention mechanism named CBAM is applied at the end of the backbone, thus not only decreasing the computation cost compared with previous works but also emphasizing the important information in both channel and spatial domain. Our result shows that HIC-YOLOv5 has improved mAP@[.5:.95] by 6.42% and mAP@0.5 by 9.38% on VisDrone-2019-DET dataset.