𝗗𝗮𝘆-𝟯𝟴𝟭 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗛𝗮𝗿𝗱𝗕𝗼𝗼𝘀𝘁: 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗛𝗮𝗿𝗱 𝗖𝗹𝗮𝘀𝘀𝗲𝘀 𝗯𝘆 𝗖𝗵𝗶𝗻𝗲𝘀𝗲 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 𝗼𝗳 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀, 𝗕𝗲𝗶𝗷𝗶𝗻𝗴 Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗛𝗮𝗿𝗱𝗕𝗼𝗼𝘀𝘁: 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗭𝗲𝗿𝗼-𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗛𝗮𝗿𝗱 𝗖𝗹𝗮𝘀𝘀𝗲𝘀 🔸 This paper is published IEEE Transaction 2022. 𝗤𝘂𝗲 : 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗭𝗲𝗿𝗼𝗦𝗵𝗼𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴? Ans : Zero-shot learning (ZSL) is a problem setup in machine learning, where at test time, a learner observes samples from classes that were not observed during training, and needs to predict the class they belong to. 𝗤𝘂𝗲 :𝗪𝗵𝗮𝘁 𝗶𝘀 𝘇𝗲𝗿𝗼 𝘀𝗵𝗼𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝗶𝘁𝘀 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀? Ans :The main advantage of this approach lies in the fact that it is able to leverage structure that exists on classes - this enables these methods to work where standard supervised learning methods fail - handling unseen/very small classes is a typical example. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances than others, as well as how to remedy the problem by detecting and exploiting hard classes. 🔸 At first, we report our empirical finding that the hard class problem is a ubiquitous phenomenon and persists regardless of used specific methods in ZSL. 🔸 Then, we find that high semantic affinity among unseen classes is a plausible underlying cause of hardness and design two metrics to detect hard classes. 🔸 Finally, two frameworks are proposed to remedy the problem by detecting and exploiting hard classes, one under inductive setting, the other under transductive setting. 🔸 The proposed frameworks could accommodate most existing ZSL methods to further significantly boost their performances with little efforts. Extensive experiments on three popular benchmarks demonstrate the benefits by identifying and exploiting the hard classes in ZSL. #computervision #artificialintelligence #innovation
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