An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Autor: | Yun Li, Zhe Liu, Lina Yao, Xianzhi Wang, Julian McAuley, Xiaojun Chang |
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Rok vydání: | 2022 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Media Technology Artificial Intelligence & Image Processing Electrical and Electronic Engineering 0801 Artificial Intelligence and Image Processing 0906 Electrical and Electronic Engineering Machine Learning (cs.LG) |
Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 32:5175-5186 |
ISSN: | 1558-2205 1051-8215 |
Popis: | Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations. A promising strategy is to learn a global-local representation that incorporates global information with extra localities (i.e., small parts/regions of inputs). However, existing methods discover localities based on explicit features without digging into the inherent properties and relationships among regions. In this work, we propose a novel Entropy-guided Reinforced Partial Convolutional Network (ERPCNet), which extracts and aggregates localities progressively based on semantic relevance and visual correlations without human-annotated regions. ERPCNet uses reinforced partial convolution and entropy guidance; it not only discovers global-cooperative localities dynamically but also converges faster for policy gradient optimization. We conduct extensive experiments to demonstrate ERPCNet's performance through comparisons with state-of-the-art methods under ZSL and Generalized Zero-Shot Learning (GZSL) settings on four benchmark datasets. We also show ERPCNet is time efficient and explainable through visualization analysis. |
Databáze: | OpenAIRE |
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