research
∙
04/06/2023
Diffusion Models as Masked Autoencoders
There has been a longstanding belief that generation can facilitate a tr...
share
research
∙
01/03/2023
Ego-Only: Egocentric Action Detection without Exocentric Pretraining
We present Ego-Only, the first training pipeline that enables state-of-t...
research
∙
12/20/2022
Unleashing the Power of Visual Prompting At the Pixel Level
This paper presents a simple and effective visual prompting method for a...
research
∙
11/21/2022
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training
Video-language pre-training is crucial for learning powerful multi-modal...
research
∙
08/25/2022
Masked Autoencoders Enable Efficient Knowledge Distillers
This paper studies the potential of distilling knowledge from pre-traine...
research
∙
07/08/2022
k-means Mask Transformer
The rise of transformers in vision tasks not only advances network backb...
research
∙
06/17/2022
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-base...
research
∙
06/15/2022
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component f...
research
∙
05/30/2022
TubeFormer-DeepLab: Video Mask Transformer
We present TubeFormer-DeepLab, the first attempt to tackle multiple core...
research
∙
05/03/2022
In Defense of Image Pre-Training for Spatiotemporal Recognition
Image pre-training, the current de-facto paradigm for a wide range of vi...
research
∙
03/22/2022
CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation
Recent advances in self-supervised contrastive learning yield good image...
research
∙
11/15/2021
iBOT: Image BERT Pre-Training with Online Tokenizer
The success of language Transformers is primarily attributed to the pret...
research
∙
11/15/2021
Searching for TrioNet: Combining Convolution with Local and Global Self-Attention
Recently, self-attention operators have shown superior performance as a ...
research
∙
06/17/2021
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a ...
research
∙
04/17/2021
H^2- Korn's Inequality and the Nonconforming Elements for The Strain Gradient Elastic Model
We establish a new H2 Korn's inequality and its discrete analog, which g...
research
∙
03/05/2021
SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
Hyperspectral imaging (HSI) unlocks the huge potential to a wide variety...
research
∙
12/01/2020
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
We present MaX-DeepLab, the first end-to-end model for panoptic segmenta...
research
∙
11/23/2020
Scaling Wide Residual Networks for Panoptic Segmentation
The Wide Residual Networks (Wide-ResNets), a shallow but wide model vari...
research
∙
10/05/2020
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Contrastive learning has been adopted as a core method for unsupervised ...
research
∙
03/17/2020
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Convolution exploits locality for efficiency at a cost of missing long r...
research
∙
11/21/2019
Rethinking Normalization and Elimination Singularity in Neural Networks
In this paper, we study normalization methods for neural networks from t...
research
∙
11/18/2019
Localizing Occluders with Compositional Convolutional Networks
Compositional convolutional networks are generative compositional models...
research
∙
05/28/2019
Compositional Convolutional Networks For Robust Object Classification under Occlusion
Deep convolutional neural networks (DCNNs) are powerful models that yiel...
research
∙
04/05/2019
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
Sketch-based image retrieval (SBIR) is widely recognized as an important...
research
∙
03/25/2019
Weight Standardization
In this paper, we propose Weight Standardization (WS) to accelerate deep...
research
∙
12/13/2018