Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances.   propose a novel framework of learning data-dependent feature transforms for scoring similarity beten an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly  propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration  design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement.  test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset  can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%.
['Ziming Zhang', 'Venkatesh Saligrama']
I am writing a research paper on a idea Learning Joint Feature Adaptation for Zero-Shot Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Learning Joint Feature Adaptation for Zero-Shot Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances.   propose a novel framework of learning data-dependent feature transforms for scoring similarity beten an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly  propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration  design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement.  test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset  can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%."}]