This paper proposes a Neural Architecture Search (NAS) method for multimodal sequential data using a gradient-based neural architecture search method named Differentiable Neural Architecture Search (DARTS). Because Deep Neural Networks (DNNs) for multimodal data require task-specific network architecture, there is a high need for NAS for them to reduce the labor of architecture design. Experimental results using an emotion recognition dataset containing sequential data showed that the proposed method succeeded in automatically designing a network architecture with competitive performance to manually designed networks.
Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF images while not considering temporal information, i.e., LF videos. This paper proposes an end-to-end neural network architecture for depth estimation from 4D LF videos. This study also constructs a medium-scale synthetic 4D LF video dataset that can be used for training deep learning-based methods. Experimental results using synthetic and real-world 4D LF videos show that temporal information contributes to the improvement of depth estimation accuracy in noisy regions. Our dataset and source codes are available at: https://mediaeng-lfv.github.io/LFV_Disparity_Estimation/.
Two-dimensional (2D) codes are widely used for various fields such as production, logistics, and marketing thanks to their larger capacity than one-dimensional barcodes. However, they are subject to distortion when printed on non-rigid materials, such as papers and clothes. Although general 2D code decoders correct uniform distortion such as perspective distortion, it is difficult to correct non-uniform and irregular distortion of the 2D code itself. This paper proposes a decoding method for the 2D code, which models monochrome auxiliary line recognition as Markov random field, and solves it using belief propagation.
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