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10/04/2022
MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models
This paper presents MOAT, a family of neural networks that build on top ...
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07/08/2022
k-means Mask Transformer
The rise of transformers in vision tasks not only advances network backb...
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06/17/2022
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-base...
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06/15/2022
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
Panoptic image segmentation is the computer vision task of finding group...
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05/30/2022
TubeFormer-DeepLab: Video Mask Transformer
We present TubeFormer-DeepLab, the first attempt to tackle multiple core...
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07/12/2021
Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms
Self-Attention has become prevalent in computer vision models. Inspired ...
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06/17/2021
DeepLab2: A TensorFlow Library for Deep Labeling
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a ...
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12/09/2020
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
In this paper, we present ViP-DeepLab, a unified model attempting to tac...
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11/28/2020
Batch Normalization with Enhanced Linear Transformation
Batch normalization (BN) is a fundamental unit in modern deep networks, ...
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11/23/2020
Scaling Wide Residual Networks for Panoptic Segmentation
The Wide Residual Networks (Wide-ResNets), a shallow but wide model vari...
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06/03/2020
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Many modern object detectors demonstrate outstanding performances by usi...
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11/25/2019
Shape-aware Feature Extraction for Instance Segmentation
Modern instance segmentation approaches mainly adopt a sequential paradi...
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11/21/2019
Rethinking Normalization and Elimination Singularity in Neural Networks
In this paper, we study normalization methods for neural networks from t...
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09/09/2019
TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion
Despite deep convolutional neural networks' great success in object clas...
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03/25/2019
Weight Standardization
In this paper, we propose Weight Standardization (WS) to accelerate deep...
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12/02/2018
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
In this paper, we study the problem of improving computational resource ...
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11/29/2018
Progressive Recurrent Learning for Visual Recognition
Computer vision is difficult, partly because the mathematical function c...
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11/28/2018
Robust Face Detection via Learning Small Faces on Hard Images
Recent anchor-based deep face detectors have achieved promising performa...
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05/15/2018
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
This paper studies teacher-student optimization on neural networks, i.e....
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04/03/2018
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Convolution is spatially-symmetric, i.e., the visual features are indepe...
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03/15/2018
Deep Co-Training for Semi-Supervised Image Recognition
In this paper, we study the problem of semi-supervised image recognition...
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12/01/2017
Single-Shot Object Detection with Enriched Semantics
We propose a novel single shot object detection network named Detection ...
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11/25/2017
Gradually Updated Neural Networks for Large-Scale Image Recognition
We present a simple yet effective neural network architecture for image ...
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11/22/2017
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning
Convolutional neural networks (CNNs) have been generally acknowledged as...
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06/12/2017