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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...
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01/05/2023
CiT: Curation in Training for Effective Vision-Language Data
Large vision-language models are generally applicable to many downstream...
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01/02/2023
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Driven by improved architectures and better representation learning fram...
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12/19/2022
Scalable Diffusion Models with Transformers
We explore a new class of diffusion models based on the transformer arch...
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10/13/2022
Exploring Long-Sequence Masked Autoencoders
Masked Autoencoding (MAE) has emerged as an effective approach for pre-t...
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01/10/2022
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of V...
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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 ...
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12/23/2021
SLIP: Self-supervision meets Language-Image Pre-training
Recent work has shown that self-supervised pre-training leads to improve...
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12/16/2021
Masked Feature Prediction for Self-Supervised Visual Pre-Training
We present Masked Feature Prediction (MaskFeat) for self-supervised pre-...
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11/22/2021
Benchmarking Detection Transfer Learning with Vision Transformers
Object detection is a central downstream task used to test if pre-traine...
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11/11/2021
Masked Autoencoders Are Scalable Vision Learners
This paper shows that masked autoencoders (MAE) are scalable self-superv...
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04/22/2021
Pri3D: Can 3D Priors Help 2D Representation Learning?
Recent advances in 3D perception have shown impressive progress in under...
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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...
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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...
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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...
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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...
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07/13/2020
Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between n...
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04/12/2020
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
Differentiable Neural Architecture Search (DNAS) has demonstrated great ...
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03/26/2020
Are Labels Necessary for Neural Architecture Search?
Existing neural network architectures in computer vision — whether desig...
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11/13/2019
Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representati...
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10/21/2019
Decoupling Representation and Classifier for Long-Tailed Recognition
The long-tail distribution of the visual world poses great challenges fo...
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06/17/2019
Sample-Efficient Neural Architecture Search by Learning Action Space
Neural Architecture Search (NAS) has emerged as a promising technique fo...
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05/30/2019
On Network Design Spaces for Visual Recognition
Over the past several years progress in designing better neural network ...
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04/02/2019
Exploring Randomly Wired Neural Networks for Image Recognition
Neural networks for image recognition have evolved through extensive man...
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12/13/2017
Rethinking Spatiotemporal Feature Learning For Video Understanding
In this paper we study 3D convolutional networks for video understanding...
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11/16/2016
Aggregated Residual Transformations for Deep Neural Networks
We present a simple, highly modularized network architecture for image c...
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04/24/2015
Holistically-Nested Edge Detection
We develop a new edge detection algorithm that tackles two important iss...
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09/18/2014
