A Baseline for Visual Instance Retrieval with Deep Convolutional Networks
Year: 2014
Type: preprint
Abstract: This paper presents a simple pipeline for visual instance retrieval exploiting image representations based on convolutional networks (ConvNets), and demonstrates that ConvNet image representations outperform other state-of-the-art image representations on six standard image retrieval datasets for the first time. Unlike existing design choices, our image representation does not require fine-tuning or learning with data similar to the test set. Furthermore, we consider the challenge Can you construct a tiny image representation with memory requirements less than or equal to 32 bytes that can successfully perform retrieval? We report the promising performance of our tiny ConvNet based representation.
Source: arXiv (Cornell University)
Institution: KTH Royal Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2164022341