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08/04/2023
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Open-vocabulary segmentation is a challenging task requiring segmenting ...
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06/29/2023
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask t...
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06/02/2023
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
Observing the close relationship among panoptic, semantic and instance s...
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04/10/2023
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-le...
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03/30/2023
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
There has been a recent explosion of computer vision models which perfor...
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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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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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02/23/2021
STEP: Segmenting and Tracking Every Pixel
In this paper, we tackle video panoptic segmentation, a task that requir...
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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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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...
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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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10/23/2020
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Recognition of human poses and activities is crucial for autonomous syst...
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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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05/20/2020
Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Supervised learning in large discriminative models is a mainstay for mod...
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05/20/2020
Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Supervised learning in large discriminative models is a mainstay for mod...
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03/17/2020
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Convolution exploits locality for efficiency at a cost of missing long r...
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12/02/2019
View-Invariant Probabilistic Embedding for Human Pose
Depictions of similar human body configurations can vary with changing v...
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11/22/2019
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast ...
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10/15/2019
SegSort: Segmentation by Discriminative Sorting of Segments
Almost all existing deep learning approaches for semantic segmentation t...
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10/10/2019
Panoptic-DeepLab
We present Panoptic-DeepLab, a bottom-up and single-shot approach for pa...
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08/26/2019
SPGNet: Semantic Prediction Guidance for Scene Parsing
Multi-scale context module and single-stage encoder-decoder structure ar...
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05/06/2019
Searching for MobileNetV3
We present the next generation of MobileNets based on a combination of c...
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02/25/2019
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
Many of the recent successful methods for video object segmentation (VOS...
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02/13/2019
DeeperLab: Single-Shot Image Parser
We present a single-shot, bottom-up approach for whole image parsing. Wh...
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01/10/2019
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Recently, Neural Architecture Search (NAS) has successfully identified n...
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09/11/2018
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
The design of neural network architectures is an important component for...
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03/22/2018
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimatio...
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02/07/2018
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Spatial pyramid pooling module or encode-decoder structure are used in d...
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01/13/2018
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
In this paper we describe a new mobile architecture, MobileNetV2, that i...
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01/13/2018
Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation
In this paper we describe a new mobile architecture, MobileNetV2, that i...
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12/13/2017
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
In this work, we tackle the problem of instance segmentation, the task o...
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07/18/2017
The Devil is in the Decoder
Many machine vision applications require predictions for every pixel of ...
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06/17/2017
Rethinking Atrous Convolution for Semantic Image Segmentation
In this work, we revisit atrous convolution, a powerful tool to explicit...
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06/02/2016
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Dee...
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11/10/2015
Attention to Scale: Scale-aware Semantic Image Segmentation
Incorporating multi-scale features in fully convolutional neural network...
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11/10/2015
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Deep convolutional neural networks (CNNs) are the backbone of state-of-a...
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02/09/2015
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
Deep convolutional neural networks (DCNNs) trained on a large number of ...
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12/22/2014
