Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition

• URL: http://arxiv.org/abs/2401.07061v1
• Date: Sat, 13 Jan 2024 12:32:29 GMT
• Title: Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition
• Authors: Hefeng Wu, Guangzhi Ye, Ziyang Zhou, Ling Tian, Qing Wang, Liang Lin
• Abstract summary: We propose a framework that exploits semantic relations to guide dual-view data hallucination for few-shot image recognition. An instance-view data hallucination module hallucinates each sample of a novel class to generate new data. A prototype-view data hallucination module exploits semantic-aware measure to estimate the prototype of a novel class.
• Score: 52.19737194653999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizability. One promising but underexplored solution is to compensate the novel classes by generating plausible samples. However, most existing works of this line exploit visual information only, rendering the generated data easy to be distracted by some challenging factors contained in the few available samples. Being aware of the semantic information in the textual modality that reflects human concepts, this work proposes a novel framework that exploits semantic relations to guide dual-view data hallucination for few-shot image recognition. The proposed framework enables generating more diverse and reasonable data samples for novel classes through effective information transfer from base classes. Specifically, an instance-view data hallucination module hallucinates each sample of a novel class to generate new data by employing local semantic correlated attention and global semantic feature fusion derived from base classes. Meanwhile, a prototype-view data hallucination module exploits semantic-aware measure to estimate the prototype of a novel class and the associated distribution from the few samples, which thereby harvests the prototype as a more stable sample and enables resampling a large number of samples. We conduct extensive experiments and comparisons with state-of-the-art methods on several popular few-shot benchmarks to verify the effectiveness of the proposed framework.

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