CSS

Summary

Cascading Style Sheets (CSS) is a style sheet language used for describing the presentation of a document written in a markup language such as HTML or XML (including XML dialects such as SVG, MathML or XHTML). CSS is a cornerstone technology of the World Wide Web, alongside HTML and JavaScript. CSS is designed to enable the separation of content and presentation, including layout, colors, and fonts. This separation can improve content accessibility; provide more flexibility and control in the specification of presentation characteristics; enable multiple web pages to share formatting by specifying the relevant CSS in a separate .css file, which reduces complexity and repetition in the structural content; and enable the .css file to be cached to improve the page load speed between the pages that share the file and its formatting. Separation of formatting and content also makes it feasible to present the same markup page in different styles for different rendering methods, such as on-s

Official source

https://en.wikipedia.org/wiki/CSS

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Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Victor Constantin, Pascal Fua, Sina Honari, Helge Jochen Rhodin, Mathieu Salzmann

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich latent vectors. Instead of simply treating the latent features of nearby frames as positive pairs and those of temporally-distant ones as negative pairs as in other CSS approaches, we explicitly disentangle each latent vector into a time-variant component and a time-invariant one. We then show that applying contrastive loss only to the time-variant features and encouraging a gradual transition on them between nearby and away frames while also reconstructing the input, extract rich temporal features, well-suited for human pose estimation. Our approach reduces error by about 50% compared to the standard CSS strategies, outperforms other unsupervised single-view methods and matches the performance of multi-view techniques. When 2D pose is available, our approach can extract even richer latent features and improve the 3D pose estimation accuracy, outperforming other state-of-the-art weakly supervised methods.

2022

Fast Object Detection with Entropy-Driven Evaluation

Carlos Joaquin Becker, François Fleuret, Pascal Fua, Raphael Sznitman

Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to estimate rejection thresholds. These characteristics are often prohibitive and thus limit their applicability. We introduce an alternative approach to speeding-up classifier evaluation which overcomes these limitations. It involves maintaining a probability estimate of the class label at each intermediary response and stopping when the corresponding uncertainty becomes small enough. As a result, the evaluation terminates early based on the sequence of responses observed. Furthermore, it does so independently of the type of ensemble classifier used or the way it was trained. We show through extensive experimentation that our method provides 2 to 10 fold speed-ups, over existing state-of-the-art methods, at almost no loss in accuracy on a number of object classification tasks.

2013

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2013