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3D scanners generate irregularly distributed cloud of points in
most of the cases. Dealing with such data, often in the form of
triangular meshes, requires a pre-processing step to regularize
the triangle facets shape and size. In this paper, we propose
CSIOR, a novel mesh regularization technique which is capable
of producing quasi-equilateral triangles, and distinguished
by two novel features, namely, its intrinsic ordered aspect and
its preservation of the geometric texture of the surface (relief

Advances in federated learning and edge computing advocate for deep learning models to run at edge devices for video analysis. However, the captured video frame rate is too high to be processed at the edge in real-time with a typical model such as CNN. Any approach to consecutively feed frames to the model compromises both the quality (by missing important frames) and the efficiency (by processing redundantly similar frames) of analysis.

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system.

The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications. Hence, it is of paramount importance to develop interpretable deep learning models. In this paper, we consider the problem of blind deconvolution and propose a novel model-aware deep architecture that allows for the recovery of both the blur kernel and the sharp image from the blurred image.

In this paper, we estimate depth information using two defocused images from dual aperture camera. Recent advances in deep learning techniques have increased the accuracy of depth estimation. Besides, methods of using a defocused image in which an object is blurred according to a distance from a camera have been widely studied. We further improve the accuracy of the depth estimation by training the network using two images with different degrees of depth-of-field.

The recent success of the Transformer based sequence-to-sequence framework for various Natural Language Processing tasks has motivated its application to Automatic Speech Recognition. In this work, we explore the application of Transformers on low resource Indian languages in a multilingual framework. We explore various methods to incorporate language information into a multilingual Transformer, i.e.,(i) at the decoder, (ii) at the encoder. These methods include using language identity tokens or providing language information to the acoustic vectors.

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping).