Enabling Large-Scale Image Search with Co-Attention Mechanism

TY - GEN

T1 - Enabling Large-Scale Image Search with Co-Attention Mechanism

AU - Hu, Zechao

AU - Bors, Adrian Gheorghe

N1 - © IEEE, 2023. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2023/6/4

Y1 - 2023/6/4

N2 - 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.

AB - 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.

U2 - 10.1109/ICASSP49357.2023.10095901

DO - 10.1109/ICASSP49357.2023.10095901

M3 - Conference contribution

BT - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

T2 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Y2 - 4 June 2023 through 10 June 2023

ER -