Abstract
There has been significant progress in content-based image retrieval with the development of convolutional neural networks and visual transformers. However, there are semantic gaps between high-level semantic information and low-level visual features. To solve this problem, we propose a high-performance image retrieval method based on the convolutional neural network (CNN) and vision transformers, which takes advantage of the local characteristics of the CNN and the long-range dependence characteristics of vision transformers. The proposed convolution and vision transformers network (CVTNet) firstly uses the CNN backbone network to extract the feature representation of the image. Secondly, it uses the vision transformers to enhance the semantic relationship among the feature layer to reduce the semantic gap. Finally, we propose an adaptive weight loss function that fuses triplet loss and second-order similarity loss to capture more image structure information. Extensive experimental results demonstrated that CVTNet achieves significant performance improvement on Revisited Oxford and Paris datasets compared with the baselines.
Supported by the National Key Research and Development Program (2018YFE0122900), the National Natural Science Foundation of China (61773224, 62066033), the Applied Technology Research and Development Foundation of Inner Mongolia Autonomous Region (2019GG372, 2020GG0046, 2021GG0158, 2020PT0002), the Achievements Transformation Project of Inner Mongolia Autonomous Region (2019CG028), the Natural Science Foundation of Inner Mongolia Autonomous Region (2020BS06001), the Science Foundation of Inner Mongolia College and University (NJZY20008).
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Zhang, Q., Bao, F., Su, X., Wang, W., Gao, G. (2022). End-to-End Large-Scale Image Retrieval Network with Convolution and Vision Transformers. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_52
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