A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects

By Xiaoyang Wang, Qiang Ji. 2013

79 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Transductive Multi-View Zero-Shot Learning (2015, methodology): “They can be roughly categorised according to the semantic representation(s) used: DAP and IAP ([27], [28]), M2LATM [15], ALE [1], [36] and [50] use attributes only; HLE/AHLE [1] and Mo/Ma/O/D [38] use both attributes and linguistic knowledge bases (same as us); [54] uses attribute and some…”
Evaluation of output embeddings for fine-grained image classification (2014, neutral): “5 S BN [30] attributes ({0, 1}) 43.”
Action Modifiers: Learning From Adverbs in Instructional Videos (2019, background): “Learning adjectives for nouns has been investigated in the context of recognizing object-attribute pairs [4, 7, 20, 34, 36, 37, 50, 51] from images.”
Synthesized Classifiers for Zero-Shot Learning (2016, background): “In the embedding-based approaches, one first maps the input image representations to the semantic space, and then determines the class labels in this space by various relatedness measures implied by the class embeddings [1, 2, 10, 11, 13, 19, 22, 25, 30, 40, 43].”
Generalized Zero-Shot Learning with Deep Calibration Network (2018, methodology): “Zero-shot learning was first studied by exploring visual attributes as semantic descriptions [12, 1, 16, 17, 30, 38, 32, 51, 4, 56].”
Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation (2014, unknown): “2* [35] 43.”
Small Sample Learning in Big Data Era (2018, unknown): “Variants like topic model (Yu and Aloimonos, 2010), random forests (Jayaraman and Grauman, 2014), and Bayesian networks (Wang and Ji, 2013) have been explored.”
Zero-Shot Learning via Semantic Similarity Embedding (2015, unknown): “[16, 39] proposed several probabilistic attribute prediction methods.”
Zero-Shot Object Detection: Joint Recognition and Localization of Novel Concepts (2020, background): “(a) The first type of methods attempt to predict classspecific semantic embeddings (Palatucci et al. 2009; Wang and Ji 2013; Lampert et al. 2014; Yu et al. 2013).”