Attribute Learning for Understanding Unstructured Social Activity

By Yanwei Fu, Timothy M. Hospedales, Tao A. Xiang, Shaogang Gong. 2012

118 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Transductive Multi-View Zero-Shot Learning (2015, methodology): “In this case a word space is extracted from linguistic knowledge bases e.g., Wikipedia by natural language processing models such as [4], [32].”
Zero-Shot Object Detection (2018, background): “Zero-shot learning has been shown to be effective in situations where there is a lack of annotated data [12,14,31,38,53,54,59,60].”
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response (2013, background): “Another approach is to represent activities as collections of semantic attributes [18], [19], [20], [21].”
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks (2014, background): “The concepts being modeled are mostly objects [31], scenes [27], or activities [10].”
A Semantical & Analytical Approach for Zero Shot Gesture Learning (2017, background): “Some of the fields where the concept has been applied include: object recognition [8], [9], neural activity recognition [10], automatically estimating human pose [11] and recognizing human actions [12] from images and from videos [13].”
Zero-shot Text Classification via Reinforced Self-training (2020, background): “There exists some work to utilize unlabeled data in image classiﬁcation to alleviate domain shift problem, including (Fu et al., 2012; Rohrbach et al., 2013; Li et al., 2015; Fu et al., 2015), etc.”
A Survey of Zero-Shot Learning (2019, methodology): “For video recognition problems, widely used datasets include UCF101 [137], USAA [39], and HMDB51 [78].”
A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects (2013, background): “Attributes are widely used in many different applications like object describing [6], zero-shot learning [17, 11], face verification [15], object recognition [34, 10], person and clothing describing [1, 2], image search and retrieval [16, 13, 36], and action/activity recognition [19, 35, 9] etc.”
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder (2015, background): “Nevertheless, for certain tasks, such shared attributes [14], [12], [25], [10], [46] may simply be unavailable or nontrivial to deﬁne.”
Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Attributes have been successfully applied for solving various computer vision problems by classification [11, 20, 22, 24], but have never been used for regression to the best of our knowledge.”
Interestingness Prediction by Robust Learning to Rank (2014, background): “In a broader sense of attributes [26,11,12,27,13] interestingness can be considered as one type of relative attributes [35], although those attributes, such as how smiling a person is, are much less subjective.”
Semi-supervised Vocabulary-Informed Learning (2016, unknown): “ZSL was first explored with use of attributebased semantic representations [11, 15, 17, 18, 24, 32].”
A Review of Human Activity Recognition Methods (2015, unknown): “…networking CCV (Jiang et al., 2011) 2001 20 9.317 FPSI (Fathi et al., 2012) 2012 6 8 ≈42 h 1,280×720 Broadcast field hockey (Lan et al., 2012b) 2012 11 58 USAA (Fu et al., 2012) 2012 8 ≈200 Sports-1M (Karpathy et al., 2014) 2014 487 1M ActivityNet (Heilbron et al., 2015) 2015 203…”
Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation (2014, methodology): “We used exactly the same RGB colour histograms, SIFT, rgSIFT, PHOG, SURF and local self-similarity histograms in [18] for AwA, and SIFT, MFCC and STIP as low-level features for USAA as in [8].”