🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |
Day-70 Computer Vision Learning IDW-CNN — Learning from Image Descriptions in the Wild Dataset Boosts the Accuracy (Semantic Segmentation) by Sun-Yat-sen University, The Chinese University of Hong Kong, and SenseTime Group (Limited) Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2017 CVPR , which has already got over 49 citations. 🔸 It outperforms FCN, CRF-RNN and DeepLabv2. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eEJ3_d3 Official Code : https://bit.ly/3rEB2YL ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Segmentation accuracy is increased by learning from an Image Descriptions in the Wild (IDW) dataset. 🔸 Unlike previous image captioning datasets, where captions were manually and densely annotated, images and their descriptions in IDW are automatically downloaded from Internet without any manual cleaning and refinement. #computervision #artificialintelligence #analytics
🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |
3yFor previous post visit this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post