Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results …
Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series …
Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of …
X Yu, L Tang, Y Rao, T Huang… - Proceedings of the …, 2022 - openaccess.thecvf.com
We present Point-BERT, a novel paradigm for learning Transformers to generalize the concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling …
K He, C Gan, Z Li, I Rekik, Z Yin, W Ji, Y Gao, Q Wang… - Intelligent …, 2023 - Elsevier
Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. In the field of medical image analysis …
Z Dai, H Liu, QV Le, M Tan - Advances in neural information …, 2021 - proceedings.neurips.cc
Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks. In this work, we show that while Transformers tend to …
Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural …
Transformer, an attention-based encoder–decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some …
J Yang, C Li, X Dai, J Gao - Advances in Neural Information …, 2022 - proceedings.neurips.cc
We propose focal modulation networks (FocalNets in short), where self-attention (SA) is completely replaced by a focal modulation module for modeling token interactions in vision …