DeepNLP commited on
Commit
a4222e3
·
verified ·
1 Parent(s): fcdbd89

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +62 -3
README.md CHANGED
@@ -1,3 +1,62 @@
1
- ---
2
- license: mit
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ task_categories:
4
+ - text-generation
5
+ language:
6
+ - en
7
+ tags:
8
+ - AI
9
+ - CVPR
10
+ ---
11
+
12
+ ## CVPR 2020 Accepted Paper Meta Info Dataset
13
+
14
+ 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.
15
+
16
+
17
+ Equations Latex code and Papers Search Engine
18
+ ![AI Equations and Search Portal](https://raw.githubusercontent.com/AI-Agent-Hub/AI-Agent-Marketplace/refs/heads/main/docs/equation_search_portal.jpg)
19
+
20
+
21
+ ### Meta Information of Json File of Paper
22
+
23
+ ```
24
+ {
25
+ "title": "Dual Super-Resolution Learning for Semantic Segmentation",
26
+ "authors": "Li Wang, Dong Li, Yousong Zhu, Lu Tian, Yi Shan",
27
+ "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.",
28
+ "pdf": "https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.pdf",
29
+ "bibtex": "https://openaccess.thecvf.com",
30
+ "url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
31
+ "detail_url": "https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Dual_Super-Resolution_Learning_for_Semantic_Segmentation_CVPR_2020_paper.html",
32
+ "tags": "CVPR 2020"
33
+ }
34
+
35
+ ```
36
+
37
+
38
+ ## Related
39
+
40
+ ## AI Agent Marketplace and Search
41
+ [AI Agent Marketplace and Search](http://www.deepnlp.org/search/agent) <br>
42
+ [Robot Search](http://www.deepnlp.org/search/robot) <br>
43
+ [Equation and Academic search](http://www.deepnlp.org/search/equation) <br>
44
+ [AI & Robot Comprehensive Search](http://www.deepnlp.org/search) <br>
45
+ [AI & Robot Question](http://www.deepnlp.org/question) <br>
46
+ [AI & Robot Community](http://www.deepnlp.org/community) <br>
47
+ [AI Agent Marketplace Blog](http://www.deepnlp.org/blog/ai-agent-marketplace-and-search-portal-reviews-2025) <br>
48
+
49
+ ## AI Agent Reviews
50
+ [AI Agent Marketplace Directory](http://www.deepnlp.org/store/ai-agent) <br>
51
+ [Microsoft AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-microsoft-ai-agent) <br>
52
+ [Claude AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-claude-ai-agent) <br>
53
+ [OpenAI AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-openai-ai-agent) <br>
54
+ [Saleforce AI Agents Reviews](http://www.deepnlp.org/store/pub/pub-salesforce-ai-agent) <br>
55
+ [AI Agent Builder Reviews](http://www.deepnlp.org/store/ai-agent/ai-agent-builder) <br>
56
+
57
+ ## AI Equation
58
+ [List of AI Equations and Latex](http://www.deepnlp.org/equation/category/ai) <br>
59
+ [List of Math Equations and Latex](http://www.deepnlp.org/equation/category/math) <br>
60
+ [List of Physics Equations and Latex](http://www.deepnlp.org/equation/category/physics) <br>
61
+ [List of Statistics Equations and Latex](http://www.deepnlp.org/equation/category/statistics) <br>
62
+ [List of Machine Learning Equations and Latex](http://www.deepnlp.org/equation/category/machine%20learning) <br>