429 citations · 2.5k across the 69 of their papers we have counts for
16 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…
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
Xiaopeng Jiang, Han Hu, Vijaya Datta Mayyuri +6
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL m…
Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm
Tengfei Zhao, Zhaocheng Ge, Hanping Hu +1
The research of adversarial attacks in the text domain attracts many interests in the last few years, and many methods with a high attack success rate have been proposed. However,…
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
Hanzhe Hu, Fangyun Wei, Han Hu +3
Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e.g., tailed categories in Cityscapes…
Video Swin Transformer
Ze Liu, Jia Ning, Yue Cao +4
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchm…
End-to-End Semi-Supervised Object Detection with Soft Teacher
Mengde Xu, Zheng Zhang, Han Hu +5
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The end-to-end training gradually improves ps…
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Fangyun Wei, Yue Gao, Zhirong Wu +2
Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. Such generality for transfer learning, however, sacrific…
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…
TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system
Kaixin Zhang, Hongzhi Wang, Han Hu +4
Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usual…
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
Yansong Tang, Zhenyu Jiang, Zhenda Xie +4
Previous cycle-consistency correspondence learning methods usually leverage image patches for training. In this paper, we present a fully convolutional method, which is simpler and…
Group-Free 3D Object Detection via Transformers
Ze Liu, Zheng Zhang, Yue Cao +2
Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usu…
Capsule Network is Not More Robust than Convolutional Network
Jindong Gu, Volker Tresp, Han Hu
The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unk…
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…