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By E. Klarreich. 2014

211 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Information Systems

In Signal Processing

In Social Sciences & Humanities

Citations in the corpus

Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books (2015, background): “Early work on movie-to-text alignment include dynamic time warping for aligning movies to scripts with the help of subtitles [5, 4].”
Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Other related tasks are, for example, face clustering [33, 26], finding important people [34], or associating names in text to faces in images [35, 36].”
Beyond frontal faces: Improving Person Recognition using multiple cues (2015, neutral): “In [9, 29, 8], the authors proposed approach to automatically label the characters by using aligned subtitle and script text.”
Random Forests for Real Time 3D Face Analysis (2012, methodology): “To improve accuracy and reduce the influence of wrong detections, global models of the facial features configuration like pictorial structures (Felzenszwalb and Huttenlocher 2005; Everingham et al. 2006) or Active Shape Models (Cristinacce and Cootes 2008) are needed.”
“Knock! Knock! Who is it?” probabilistic person identification in TV-series (2012, methodology): “using speaker detection as in [7, 14], we expect a more significant impact in the future.”
Detecting People Looking at Each Other in Videos (2013, background): “1; and, (iii) the length of the temporal window W in the range [5,11] in steps of 2.”
Face detection, pose estimation, and landmark localization in the wild (2012, methodology): “(4) Oxford facial landmark detector [13] reports 9 facial landmarks: corners of eyes, nostrils, nose tip and mouth corners.”
It ’ s in the bag : Stronger supervision for automated face labelling (2015, methodology): “This topic has received plenty of attention in the community and papers have investigated the problem under various assumptions on the available information [2, 3, 5, 4, 6, 8, 9, 12, 17, 18, 20, 22, 24].”
Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification (2012, background): “Similar parts-based features are demonstrated for face recognition by [1, 8, 34].”
Lip Reading in the Wild (2016, methodology): “The BBC transmits subtitles as bitmaps rather than text, therefore subtitle text is extracted from the broadcast video using standard OCR methods [2, 6].”
Condensed Movies: Story Based Retrieval with Contextual Embeddings (2020, background): “Early works, however, focused on using film and TV to learn human identity [21,22,23,24,25,26] or human actions [27,28,29,30,31] from the scripts or captions accompanying movies.”
Localizing Parts of Faces Using a Consensus of Exemplars (2013, methodology): “Like [9], we do not believe that a joint distribution of part locations over a wide range of poses is adequately modeled by a single Gaussian, but instead we take a non-parametric approach and use the part locations in a large number of labeled exemplar images to model the joint distribution.”
Channel-Level Acceleration of Deep Face Representations (2015, methodology): “Specifically, we use the method proposed by [36], which detects 9 facial features followed by an affine transform”