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In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder.

Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression.

Image compression approaches based on deep learning have achieved remarkable success.
Existing studies mainly focus on human vision and machine analysis tasks taking reconstructed images as input.

Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NN) complexity that can be supported. Thus, NN compression mechanisms are deemed critical for the efficient deployment of such demanding tasks.

Image compression is a key problem in this age of information explosion. With the help of machine learning, recent studies have shown that learning-based image compression methods tend to surpass traditional codecs. Image compression can be split into three steps: transform, quantization, and entropy estimation.

Object pose estimation remains an open and important task for autonomous systems, allowing them to perceive and interact with the surrounding environment. To this end, this paper proposes a 3D object pose estimation method that is suitable for execution on embedded systems. Specifically, a novel multi-task objective function is proposed, in order to train a Convolutional Neural Network (CNN) to extract pose-related features from RGB images, which are subsequently utilized in a Nearest-Neighbor (NN) search-based post-processing step to obtain the final 3D object poses.

In this paper, we propose a framework for 3D human pose estimation with a single 360° camera mounted on the user's wrist. Perceiving a 3D human pose with such a simple setting has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports enhancement). However, no existing work has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured and the lack of training data.