Joint People, Event, and Location Recognition in Personal Photo Collections Using Cross-Domain Context

By Dahua Lin, Ashish Kapoor, Gang Hua, Simon Baker. 2010

62 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Various other cues have been explored, for example: attributes classification [40, 41], social context [42, 43], relative camera positions [44], space-time priors [45], and photo-album priors [46].”
Beyond frontal faces: Improving Person Recognition using multiple cues (2015, background): “[24] presented a generative probabilistic approach to model crossdomain relationships to jointly tag people, events and locations.”
Person Recognition in Personal Photo Collections (2015, background): “Multiple other cues have been explored, for example: attributes classification [24, 25], explicit cloth modelling [15], relative camera positions [18], social context [16, 35], space-time priors [28], and photo-album priors [34].”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Exploring Tiny Images: the Roles of Appearance and Contextual Information for Machine and Human Object Recognition (unknown): “Gallagher et al. [29] and Lin et al. [57] propose the use of other types of contextual information, such as social context for analyzing personal photo collections.”
Describing Clothing by Semantic Attributes (2012, background): “Another important application is context-aware person identification, where many researchers have demonstrated superior performance by incorporating clothing information as a contextual cue that complements facial features [2–4].”
Geography-Aware Self-Supervised Learning (2020, methodology): “ges, geolocation metadata, to further improve the quality of the learned representations. Geo-location metadata has been previously explored by different studies to improve image recognition accuracy [37,23,7,20,6], image retrieval [11] and predict geo-location of the images missing meta-data [13,14”