Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. Hover, less research attention has been focused on multi-class active learning. ,  propose a novel Gaussian process classifier model with multiple annotators for multi-class visual recognition. Expectation propagation (EP) is adopted for efficient approximate Bayesian inference of our probabilistic model for classification. Based on the EP approximation inference, a generalized Expectation Maximization (GEM) algorithm is derived to estimate both the parameters for instances and the quality of each individual annotator. Also,  incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality annotators, which better explores the trade-off beten exploitation and exploration. The experiments clearly demonstrate the efficacy of the proposed model.
['Chengjiang Long', 'Gang Hua']
I am writing a research paper on a idea Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Active learning is an effective way to relieve the tedious work of manual annotation in many applications of visual recognition. Hover, less research attention has been focused on multi-class active learning. ,  propose a novel Gaussian process classifier model with multiple annotators for multi-class visual recognition. Expectation propagation (EP) is adopted for efficient approximate Bayesian inference of our probabilistic model for classification. Based on the EP approximation inference, a generalized Expectation Maximization (GEM) algorithm is derived to estimate both the parameters for instances and the quality of each individual annotator. Also,  incorporate the idea of reinforcement learning to actively select both the informative samples and the high-quality annotators, which better explores the trade-off beten exploitation and exploration. The experiments clearly demonstrate the efficacy of the proposed model."}]