Zero-Shot Recognition via Structured Prediction

By Ziming Zhang, Venkatesh Saligrama. 2016

99 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering, Representation & Self-Supervised Learning

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Citations in the corpus

Prototypical Networks for Few-shot Learning (2017, methodology): “Our Protypical Networks outperform Synthesized Classifiers and are within error bars of Zhang and Saligrama [36], while being a much simpler approach than either.”
Learning to Compare: Relation Network for Few-Shot Learning (2017, background): “cent methods [48, 12, 13] are tranductive in that they use all test data at once for model training, which gives them a big advantage at the cost of making a very strong assumption that may not be met in practical applications, so we do not compare with them here.”
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly (2017, background): “Apart from the inductive zero-shot learning set-up where the model has no access to neither visual nor side-information of unseen classes, transductive zero-shot learning approaches [28], [44], [45], [46], [47], [48], [49] use visual or semantic information of both seen and unseen classes without ha”
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].”
Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification (2018, background): “For combo baseline, we average the test decision values from the most competitive WSL baseline (i.e., Xiao et al.) and ZSL baseline (i.e., Zhang and Saligrama) in Table 1 for comparison, which is referred to as WSL+ZSL.”
Domain-Invariant Projection Learning for Zero-Shot Recognition (2018, methodology): “A variant is the structured prediction model [69] which employs a Gaussian parametrization of the unseen class domain label predictions.”
Learning a Deep Embedding Model for Zero-Shot Learning (2016, methodology): “Some recent methods [49, 11, 12] are tranductive in that they use all test data at once for model training, which gives them a big unfair advantage.”
A Simple Exponential Family Framework for Zero-Shot Learning (2017, methodology): “Transductive Setting For transductive ZSL setting [9,35,36], we follow the procedure described in Section 2.”
Learning Robust Visual-Semantic Embeddings (2017, methodology): “A number of similar methods learn transformations from input image representations to the semantic space for the recognition or retrieval purposes [2, 49, 1, 48, 7, 50, 51, 30, 37, 6, 12].”
Zero-Shot Semantic Segmentation (2019, background): “Note that there exists a connection between ZS5Net and transductive zero-shot learning [26, 40, 49], but that our model is not transductive.”
Zero Shot Detection (2018, background): “ZERO shot learning (ZSL) has recently drawn increasing attention [2]–[5].”