Investigating The Vision Transformer Model For Image Retrieval Tasks

Abstract

This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Transformer network while it does not require any training data to adjust parameters. In image retrieval tasks, the use of Handcrafted global and local descriptors has been very successfully replaced, over the last years, by the Convolutional Neural Networks (CNN)-based methods. However, the experimental evaluation conducted in this paper on several benchmarking datasets against 36 state-of-the-art descriptors from the literature demonstrates that a neural network that contains no convolutional layer, such as Vision Transformer, can shape a global descriptor and achieve competitive results. As fine-tuning is not required, the presented methodology's low complexity encourages adoption of the architecture as an image retrieval baseline model, replacing the traditional and well a

Stats

• citations26
• S2 citations—
• github stars0
• HF likes0
• heat score10.74
• arxiv keygkelios2021investigating

Related papers

• Training Vision Transformers For Image Retrieval (2021)0.00
• Boosting Vision Transformers For Image Retrieval (2022)15.28
• Vision Transformer Hashing For Image Retrieval (2021)17.01
• DALG: Deep Attentive Local And Global Modeling For Image Retrieval (2022)0.00
• Self-supervised Vision Transformers For Writer Retrieval (2024)5.24
• Convolutional Patch Representations For Image Retrieval: An Unsupervised Approach (2016)12.47
• Transhash: Transformer-based Hamming Hashing For Efficient Image Retrieval (2021)13.44
• Analyzing Local Representations Of Self-supervised Vision Transformers (2023)0.00