Saining Xie | DeepAI

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∙ 05/18/2023

Going Denser with Open-Vocabulary Part Segmentation

Object detection has been expanded from a limited number of categories t...

0 Peize Sun, et al. ∙

∙ 01/05/2023

CiT: Curation in Training for Effective Vision-Language Data

Large vision-language models are generally applicable to many downstream...

0 Hu Xu, et al. ∙

∙ 01/02/2023

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Driven by improved architectures and better representation learning fram...

0 Sanghyun Woo, et al. ∙

∙ 12/19/2022

Scalable Diffusion Models with Transformers

We explore a new class of diffusion models based on the transformer arch...

0 William Peebles, et al. ∙

∙ 10/13/2022

Exploring Long-Sequence Masked Autoencoders

Masked Autoencoding (MAE) has emerged as an effective approach for pre-t...

7 Ronghang Hu, et al. ∙

∙ 01/10/2022

A ConvNet for the 2020s

The "Roaring 20s" of visual recognition began with the introduction of V...

12 Zhuang Liu, et al. ∙

∙ 12/27/2021

A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision

Using natural language as a supervision for training visual recognition ...

2 Ajinkya Tejankar, et al. ∙

∙ 12/23/2021

SLIP: Self-supervision meets Language-Image Pre-training

Recent work has shown that self-supervised pre-training leads to improve...

12 Norman Mu, et al. ∙

∙ 12/16/2021

Masked Feature Prediction for Self-Supervised Visual Pre-Training

We present Masked Feature Prediction (MaskFeat) for self-supervised pre-...

37 Chen Wei, et al. ∙

∙ 11/22/2021

Benchmarking Detection Transfer Learning with Vision Transformers

Object detection is a central downstream task used to test if pre-traine...

17 Yanghao Li, et al. ∙

∙ 11/11/2021

Masked Autoencoders Are Scalable Vision Learners

This paper shows that masked autoencoders (MAE) are scalable self-superv...

45 Kaiming He, et al. ∙

∙ 04/22/2021

Pri3D: Can 3D Priors Help 2D Representation Learning?

Recent advances in 3D perception have shown impressive progress in under...

8 Ji Hou, et al. ∙

∙ 04/05/2021

An Empirical Study of Training Self-Supervised Vision Transformers

This paper does not describe a novel method. Instead, it studies a strai...

39 Xinlei Chen, et al. ∙

∙ 02/22/2021

On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness

Invariance to a broad array of image corruptions, such as warping, noise...

4 Eric Mintun, et al. ∙

∙ 12/16/2020

Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts

The rapid progress in 3D scene understanding has come with growing deman...

10 Ji Hou, et al. ∙

∙ 07/21/2020

PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Arguably one of the top success stories of deep learning is transfer lea...

11 Saining Xie, et al. ∙

∙ 07/13/2020

Graph Structure of Neural Networks

Neural networks are often represented as graphs of connections between n...

23 Jiaxuan You, et al. ∙

∙ 04/12/2020

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Differentiable Neural Architecture Search (DNAS) has demonstrated great ...

17 Alvin Wan, et al. ∙

∙ 03/26/2020

Are Labels Necessary for Neural Architecture Search?

Existing neural network architectures in computer vision — whether desig...

14 Chenxi Liu, et al. ∙

∙ 11/13/2019

Momentum Contrast for Unsupervised Visual Representation Learning

We present Momentum Contrast (MoCo) for unsupervised visual representati...

25 Kaiming He, et al. ∙

∙ 10/21/2019

Decoupling Representation and Classifier for Long-Tailed Recognition

The long-tail distribution of the visual world poses great challenges fo...

0 Bingyi Kang, et al. ∙

∙ 06/17/2019

Sample-Efficient Neural Architecture Search by Learning Action Space

Neural Architecture Search (NAS) has emerged as a promising technique fo...

6 Linnan Wang, et al. ∙

∙ 05/30/2019

On Network Design Spaces for Visual Recognition

Over the past several years progress in designing better neural network ...

16 Ilija Radosavovic, et al. ∙

∙ 04/02/2019

Exploring Randomly Wired Neural Networks for Image Recognition

Neural networks for image recognition have evolved through extensive man...

36 Saining Xie, et al. ∙

∙ 12/13/2017

Rethinking Spatiotemporal Feature Learning For Video Understanding

In this paper we study 3D convolutional networks for video understanding...

0 Saining Xie, et al. ∙

∙ 11/16/2016

Aggregated Residual Transformations for Deep Neural Networks

We present a simple, highly modularized network architecture for image c...

0 Saining Xie, et al. ∙

∙ 04/24/2015

Holistically-Nested Edge Detection

We develop a new edge detection algorithm that tackles two important iss...

0 Saining Xie, et al. ∙

∙ 09/18/2014

Deeply-Supervised Nets

Our proposed deeply-supervised nets (DSN) method simultaneously minimize...

0 Chen-Yu Lee, et al. ∙