412 citations · 777 across the 15 of their papers we have counts for
6 papers · 1 filter
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
Mengde Xu, Zheng Zhang, Fangyun Wei +4
Recently, open-vocabulary image classification by vision language pre-training has demonstrated incredible achievements, that the model can classify arbitrary categories without se…
Bootstrap Your Object Detector via Mixed Training
Mengde Xu, Zheng Zhang, Fangyun Wei +5
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free. MixTraining enhances data augmentation by ut…
SimMIM: A Simple Framework for Masked Image Modeling
Zhenda Xie, Zheng Zhang, Yue Cao +5
This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and to…
Swin Transformer V2: Scaling Up Capacity and Resolution
Ze Liu, Han Hu, Yutong Lin +9
Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities l…
Self-Supervised Learning with Swin Transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao +4
We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers a…
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Ze Liu, Yutong Lin, Yue Cao +5
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer fro…