Titlebook: Advances in Visual Computing; 17th International S George Bebis,Bo Li,Remco Chang Conference proceedings 2022 The Editor(s) (if applicable)

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Zanisah Man,Sharina Abdul Halimbeing sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using re

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Nathan N. Gichuki,Jane M. Macharia-spectral data set is amenable to computational algorithms that highlight functional properties of the sample. Although Fourier transform IR (FT-IR) imaging provides reliable analytical information over a wide spectral profile, long data acquisition times are a major challenge impeding broad adoptab

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https://doi.org/10.1007/978-3-030-26407-9ies a series of operators in a pipeline to transform a video stream into shots. These operators are replicated to work in parallel on Flink-managed computing nodes. The V2F deployment of the standard twin-comparison video segmentation method is more than 7 times faster than its non-parallel (i.e., s

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https://doi.org/10.1007/978-3-030-26407-9h as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separat

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https://doi.org/10.1007/978-3-030-26407-9o function correctly. While previous state-of-the-art methods relied on modeling social interactions with LSTMs, with videos captured with a static camera from a bird’s-eye view, our paper presents a new method that leverages the Transformers architecture and offers a reliable way to model future tr