In this paper, we proposal a method inspired by RNN that can extract the spatial context features and design a lighter, faster and more accurate network model ...
In this paper, we proposal a method inspired by RNN that can extract the spatial context features and design a lighter, faster and more accurate network model ...
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What is the difference between convolutional neural network and RNN?
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What is convolutional neural network for image detection?
RNNs are designed to handle sequential data by processing input sequences one step at a time, maintaining a hidden state that captures information from previous ...
Nov 1, 2022 · This article proposes a novel computer vision based method of gait recognition both with and without carried objects (COs) using Faster region convolutional ...
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost.
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks. The proposed network, called ReNet ...
Feb 21, 2018 · Another example is Ming Liang and Xiaolin Hu's approche where they mix a CNN with an RNN and use this architecture for better object detection.
Oct 29, 2020 · This work compares different methods, especially those which use Recurrent Neural Networks to detect objects in videos. We differ between ...
Sep 25, 2020 · The skip pooling method [17] is used for multi-feature fusion of different deep neural network algorithms to solve the problem of small object ...
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Mar 23, 2024 · This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation ...