Z Xia, X Pan, S Song, LE Li… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher …
Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage …
Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across …
C Yang, Z Huang, N Wang - Proceedings of the IEEE/CVF …, 2022 - openaccess.thecvf.com
While general object detection with deep learning has achieved great success in the past few years, the performance and efficiency of detecting small objects are far from satisfactory …
Q Lan, D Wen, Z Zhang, Q Zeng, X Chen… - Journal of …, 2021 - ieeexplore.ieee.org
In the 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this …
Y Wang, R Huang, S Song… - Advances in neural …, 2021 - proceedings.neurips.cc
Abstract Vision Transformers (ViT) have achieved remarkable success in large-scale image recognition. They split every 2D image into a fixed number of patches, each of which is …
Current dynamic networks and dynamic pruning methods have shown their promising capability in reducing theoretical computation complexity. However, dynamic sparse …
The self-attention-based model, transformer, is recently becoming the leading backbone in the field of computer vision. In spite of the impressive success made by transformers in a …
J Xu, W Zhou, Z Fu, H Zhou, L Li - arXiv preprint arXiv:2111.05193, 2021 - arxiv.org
In recent years, larger and deeper models are springing up and continuously pushing state- of-the-art (SOTA) results across various fields like natural language processing (NLP) and …