412 citations · 777 across the 15 of their papers we have counts for
15 papers
InstructSAM: Segment Any Instance with Any Instructions
Yuqian Yuan, Wentong Li, Zhaocheng Li +6
In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven…
Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
SII-GAIR, Sand. ai, : +43
We present daVinci-MagiHuman, an open-source audio-video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized video and audio…
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
Yichao Shen, Zigang Geng, Yuhui Yuan +6
We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accu…
DETR Doesn't Need Multi-Scale or Locality Design
Yutong Lin, Yuhui Yuan, Zheng Zhang +3
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality co…
Could Giant Pretrained Image Models Extract Universal Representations?
Yutong Lin, Ze Liu, Zheng Zhang +4
Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few pa…
On Data Scaling in Masked Image Modeling
Zhenda Xie, Zheng Zhang, Yue Cao +4
An important goal of self-supervised learning is to enable model pre-training to benefit from almost unlimited data. However, one method that has recently become popular, namely ma…
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…
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
Zhenda Xie, Yutong Lin, Zheng Zhang +3
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning…
Parametric Instance Classification for Unsupervised Visual Feature Learning
Yue Cao, Zhenda Xie, Bin Liu +3
This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a…
Negative Margin Matters: Understanding Margin in Few-shot Classification
Bin Liu, Yue Cao, Yutong Lin +4
This paper introduces a negative margin loss to metric learning based few-shot learning methods. The negative margin loss significantly outperforms regular softmax loss, and achiev…