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DeepLab2: A TensorFlow Library for Deep Labeling
[article]
2021
arXiv
pre-print
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DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer vision. ...
We hope that publicly sharing our library could facilitate future research on dense pixel labeling tasks and envision new applications of this technology. Code is made publicly available at . ...
We would like to thank Michalis Raptis for the feedback on the paper, Jiquan Ngiam and Amil Merchant for Hungarian Matching implementation, and the support from Google Mobile Vision. ...
arXiv:2106.09748v1
fatcat:lbly3ld4zjc5rpq6l7l6bzkko4
Human Segmentation and Tracking Survey on Masks for MADS Dataset
2021
Sensors
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In this paper, we performed a survey of many studies, methods, datasets, and results for human segmentation and tracking in video. ...
It has been published for the task of estimating human posture. However, before determining the human pose, the person needs to be detected as a segment in the video. ...
, gcc/& g++ (≥5.4 version), In addition, there are a number of other libraries such as Numpy, scipy, Pillow, cython, matplotlib, scikit-image, tensorflow ≥ 1.3.0, keras ≥ 2.0.8, opencv-python, h5py, imgaug ...
doi:10.3390/s21248397
pmid:34960491
pmcid:PMC8706170
fatcat:nrdqatixsfg7bpmr3xwjrmmaqq
Towards End-to-End Unified Scene Text Detection and Layout Analysis
[article]
2022
arXiv
pre-print
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We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. ...
Dataset and code: https://github.com/google-research-datasets/hiertext and https://github.com/tensorflow/models/tree/master/official/projects/unified_detector. ...
Experimental settings Unified Detector: We use the DeepLab2 [56] library for the implementation of the MaX-DeepLab part of our method. ...
arXiv:2203.15143v2
fatcat:vica3fst5vg2xip2n2tw4j4u3u
Loss-Augmented Structured Learning for Semantic Labeling
[article]
2019
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This framework benefits from having flexibilities in defining user-defined loss functions as a function of the true and predicted labels, task-specific joint features dependent on the predicted label and ...
The probabilistic graphical models and deep learning models are the most commonly used schemes for semantic segmentation. We will employ SSVM for parameter learning in both of these models. ...
For this purpose, we use the OpenGM C++ library [417] . ...
doi:10.25911/5f58afd977273
fatcat:ql3pmchvd5c47pasgpwdawclsy