Inertial measurement units (IMU) in the capturing device can record the motion information of the device, with gyroscopes measuring angular velocity and accelerometers measuring acceleration. However, conventional deblurring methods seldom incorporate IMU data, and existing approaches that utilize IMU information often face challenges in fully leveraging this valuable data, resulting in noise issues from the sensors. To address these issues, in this paper, we propose a multi-stage deblurring network named INformer, which combines inertial information with the Transformer architecture. Specifically, we design an IMU-image Attention Fusion (IAF) block to merge motion information derived from inertial measurements with blurry image features at the attention level. Furthermore, we introduce an Inertial-Guided Deformable Attention (IGDA) block for utilizing the motion information features as guidance to adaptively adjust the receptive field, which can further refine the corresponding blur kernel for pixels. Extensive experiments on comprehensive benchmarks demonstrate that our proposed method performs favorably against state-of-the-art deblurring approaches.

Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [ 16 , 31 , 42 ]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that are closely connected to be similar and structural similarity, which is measured by assessing their relations to neighbors; while these methods only focus on static graphs. In this work, we bridge connectivity and structural similarity in a uniform representation via motifs, and consequently present an algorithm for Learning Embeddings by leveraging Motifs Of Networks (LEMON), which aims to learn embeddings for vertices and various motifs. Moreover, LEMON is inherently capable of dealing with inductive learning tasks for dynamic graphs. To validate the effectiveness and efficiency, we conduct various experiments on two real-world datasets and five public datasets from diverse domains. Through comparison with state-of-the-art baseline models, we find that LEMON achieves significant improvements in downstream tasks. We release our code on Github at https://github.com/larry2020626/LEMON.