Enabling Large-Scale Image Search with Co-Attention Mechanism

Abstract

Content-based image retrieval (CBIR) consists of searching the most similar images to a given query. Most existing attention mechanisms for CBIR are query non-sensitive and are only based on single candidate image’s feature regardless of the actual query content. This can result in incorrect regions especially when the target object is not salient or surrounded by distractors. This paper proposes an efficient and effective query sensitive co-attention mechanism for large scale CBIR tasks. Local feature selection and clustering are employed to reduce the computation cost caused by the query sensitivity. Experimental results indicate that the proposed co-attention method can generate good co-attention maps even under challenging situations leading to a new state of the art performance on several benchmark datasets.