Human pose estimation in computer vision is particularly challenging with images containing multiple individuals. Existing methods often integrate spatial and channel attention by simply adding them up through a cascade or parallel connection. The features extracted in this way could lead to less accurate key point predictions, especially in cases where the limbs of different people are obstructed or tangled with each other. To tackle this crucial issue, we develop a novel network that enhances key point detection by combining the spatial and channel attention in a more effective manner. Specifically, our network features a lightweight perception-enhanced module (PEM) that adaptively fuses spatial and channel features through a Hadamard product, thereby refining the overall feature representation. Moreover, by exploiting the initial feature map as a guide to generate the pixel attention, we further boost key point prediction accuracy. Extensive experimental results show that our developed network can clearly outperform the current state-of-the-art methods.

Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Benjamin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Remote sensing target tracking in UAV (Unmanned Aerial Vehicle) aerial videos has gradually become a research hotspot of UAV application areas, in this paper, we propose a remote sensing target tracking method for UAV aerial videos based on multi-frequency feature enhancement. The main contributions are as follows: first, We use SiamRPN as the basic tracking network to obtain the natural ability of multi-scale testing, which can adapt to the scale changing of remote sensing targets caused by changes in shooting perspective. Second, in the stage of feature extraction, we introduce a new multi-frequency feature representation method, which effectively improves feature expression ability of highly dynamic targets. Our algorithm is compared quantitatively and qualitatively with some state-of-the-art tracking algorithms by using a test dataset consisting of a UAV123 dataset and a homemade dataset, the results show that our algorithm improves the tracking accuracy while having strong timeliness.