420 citations · 2.4k across the 126 of their papers we have counts for
17 papers · 1 filter
Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast
Kaifeng Bi, Lingxi Xie, Hengheng Zhang +3
In this paper, we present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For this purpose, we establish a data-driven environment by dow…
Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration
Yunjie Tian, Lingxi Xie, Jihao Qiu +4
We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation mode…
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models
Chengcheng Ma, Yang Liu, Jiankang Deng +3
Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing…
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
Binghao Liu, Boyu Yang, Lingxi Xie +3
Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study,…
Fine-Grained Semantically Aligned Vision-Language Pre-Training
Juncheng Li, Xin He, Longhui Wei +6
Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarit…
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
Maosen Li, Siheng Chen, Zijing Zhang +3
Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these meth…
Active Pointly-Supervised Instance Segmentation
Chufeng Tang, Lingxi Xie, Gang Zhang +3
The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting,…
Visual Recognition by Request
Chufeng Tang, Lingxi Xie, Xiaopeng Zhang +2
Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establis…
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
Wei Shen, Zelin Peng, Xuehui Wang +6
The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithm…
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
Xiaosong Zhang, Yunjie Tian, Wei Huang +4
Recently, masked image modeling (MIM) has offered a new methodology of self-supervised pre-training of vision transformers. A key idea of efficient implementation is to discard the…
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
Jiemin Fang, Taoran Yi, Xinggang Wang +5
Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and synthesizing novel-view images. However, most previous NeRF methods take much time to optimize one…
Domain-Agnostic Prior for Transfer Semantic Segmentation
Xinyue Huo, Lingxi Xie, Hengtong Hu +3
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target dom…
CenterNet++ for Object Detection
Kaiwen Duan, Song Bai, Lingxi Xie +3
There are two mainstreams for object detection: top-down and bottom-up. The state-of-the-art approaches mostly belong to the first category. In this paper, we demonstrate that the…
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
Yunjie Tian, Lingxi Xie, Jiemin Fang +6
The past year has witnessed a rapid development of masked image modeling (MIM). MIM is mostly built upon the vision transformers, which suggests that self-supervised visual represe…
DATA: Domain-Aware and Task-Aware Self-supervised Learning
Qing Chang, Junran Peng, Lingxie Xie +4
The paradigm of training models on massive data without label through self-supervised learning (SSL) and finetuning on many downstream tasks has become a trend recently. However, d…
MVP: Multimodality-guided Visual Pre-training
Longhui Wei, Lingxi Xie, Wengang Zhou +2
Recently, masked image modeling (MIM) has become a promising direction for visual pre-training. In the context of vision transformers, MIM learns effective visual representation by…
TAPE: Task-Agnostic Prior Embedding for Image Restoration
Lin Liu, Lingxi Xie, Xiaopeng Zhang +5
Learning a generalized prior for natural image restoration is an important yet challenging task. Early methods mostly involved handcrafted priors including normalized sparsity, l_0…