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The use of mobile phones in public places opens up the possibilities of remote side channel attacks on these devices. We present a video-based side channel attack to decipher passwords on mobile devices. Our method uses short video clips ranging from 5 to 10 s each, which can be taken unobtrusively from a distance and do not require the keyboard or the screen of the phone to be visible.

Zero-shot learning (ZSL) has enjoyed great popularity in recent years due to its ability to recognize novel objects, where semantic information is exploited to build up relations among different categories. Traditional ZSL approaches usually focus on learning more robust visual-semantic embeddings among seen classes and directly apply them to the unseen classes without considering whether they are suitable.

This paper presents an intelligent system named Magic-wall, which enables visualization of the effect of room decoration automatically. Concretely, given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing wall with the required one. 

In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames.