license: mit
task_categories:
- text-generation
language:
- en
tags:
- AI
- CVPR
CVPR 2020 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2020 Open Access website (https://openaccess.thecvf.com/CVPR2020) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2020). For researchers who are interested in doing analysis of CVPR 2020 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2020 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Dual Super-Resolution Learning for Semantic Segmentation",
"authors": "Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan",
"abstract": "Current state-of-the-art semantic segmentation methods often apply high-resolution input to attain high performance, which brings large computation budgets and limits their applications on resource-constrained devices. In this paper, we propose a simple and flexible two-stream framework named Dual Super-Resolution Learning (DSRL) to effectively improve the segmentation accuracy without introducing extra computation costs. Specifically, the proposed method consists of three parts: Semantic Segmentation Super-Resolution (SSSR), Single Image Super-Resolution (SISR) and Feature Affinity (FA) module, which can keep high-resolution representations with low-resolution input while simultaneously reducing the model computation complexity. Moreover, it can be easily generalized to other tasks, e.g., human pose estimation. This simple yet effective method leads to strong representations and is evidenced by promising performance on both semantic segmentation and human pose estimation. Specifically, for semantic segmentation on CityScapes, we can achieve \\geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs. For human pose estimation, we can gain \\geq2% mAP with the same FLOPs and maintain mAP with 30% fewer FLOPs. Code and models are available at https://github.com/wanglixilinx/DSRL.",
"pdf": "https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"detail_url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
"tags": "CVPR 2020"
}
Related
AI Agent Marketplace and Search
AI Agent Marketplace and Search
Robot Search
Equation and Academic search
AI & Robot Comprehensive Search
AI & Robot Question
AI & Robot Community
AI Agent Marketplace Blog
AI Agent Reviews
AI Agent Marketplace Directory
Microsoft AI Agents Reviews
Claude AI Agents Reviews
OpenAI AI Agents Reviews
Saleforce AI Agents Reviews
AI Agent Builder Reviews
AI Equation
List of AI Equations and Latex
List of Math Equations and Latex
List of Physics Equations and Latex
List of Statistics Equations and Latex
List of Machine Learning Equations and Latex