Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟭𝟳𝟳 Computer Vision Learning 𝗔𝗔𝗕𝗢: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling by 𝗧𝘀𝗶𝗻𝗴𝗵𝘂𝗮 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗮𝗻𝗱 𝗛𝘂𝗮𝘄𝗲𝗶 𝗡𝗼𝗮𝗵’𝘀 𝗔𝗿𝗸 𝗟𝗮𝗯 Follow me for a similar post:  🇮🇳 Ashish Patel Interesting Facts : 🔸 This is a paper in ECCV2020 with over 1 citations. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/e-8b3rA Code : https://lnkd.in/emxRuFJ ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Most state-of-the-art object detection systems follow an anchor-based diagram. Anchor boxes are densely proposed over the images and the network is trained to predict the boxes position offset as well as the classification confidence. Existing systems pre-define anchor box shapes and sizes and ad-hoc heuristic adjustments are used to define the anchor configurations. 🔸 Authors design a flexible and tight hyper-parameter space for anchor configurations. Then propose a novel hyper-parameter optimization method named AABO to determine more appropriate anchor boxes for a certain dataset, in which Bayesian Optimization and sub-sampling method are combined to achieve precise and efficient anchor configuration optimization. #computervision #artificialintelligence #innovation