Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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Juan C. Pastor,James Meindl,Raymond Hunt and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed .nified .ehazing a.d .etection (UDnD) framework consists of three parts: a residual-aware haze den

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ligature

Where does Management Knowledge come from? notion of attention: how to decide what to describe and in which order. Inspired by the successes in text analysis and translation, previous works have proposed the . architecture for image captioning. However, the structure between the . in images (usually the detected regions from object detectio

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https://doi.org/10.1007/978-3-642-51559-0some limitations in modern networks, where the magnitude of parameters can vary independently of the importance of corresponding channels. To recognize redundancies more accurately and therefore, accelerate networks better, we propose a novel channel pruning criterion based on the Pearson correlatio

opalescence

The Diffusion of Electronic Data Interchangectly natural for humans, the same task has proven to be challenging for learning machines. Deep neural networks are still prone to catastrophic forgetting of previously learnt information when presented with information from a sufficiently new distribution. To address this problem, we present NeoNet

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