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Self-supervised Product Quantization for Deep Unsupervised Image Retrieval [PDF]
IEEE International Conference on Computer Vision, 2021 Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods ...
Young Kyun Jang, Nam Ik Cho
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Wireless Image Retrieval at the Edge [PDF]
IEEE Journal on Selected Areas in Communications, 2020 We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server.
Mikołaj Jankowski +2 more
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Iranian Journal of Information Processing & Management, 2014 Sometimes we have only an image that we should get our information by that. There are many search engines and software's that can search images but all of them don’t have the ability to search for similar images, image search by image.
Mahshid Eltemasi
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Zero-Shot Composed Image Retrieval with Textual Inversion [PDF]
IEEE International Conference on Computer Vision, 2023 Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two images.
Alberto Baldrati +3 more
semanticscholar +1 more source
Survey of Deep Feature Instance Level Image Retrieval Algorithms [PDF]
Jisuanji kexue yu tansuo, 2023 Content-based image retrieval algorithm (CBIR) aims to find semantically matching or similar images with query images. It analyzes visual content in a large number of image databases. It is important to obtain discriminant image representation by feature
JI Changqing, WANG Bingbing, QIN Jing, WANG Zumin
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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval [PDF]
Computer Vision and Pattern Recognition, 2023 In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the ...
Kuniaki Saito +6 more
semanticscholar +1 more source
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval [PDF]
IEEE International Conference on Computer Vision, 2021 Our objective in this work is video-text retrieval – in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design of the visual architecture and the nature of the training data, in that ...
Max Bain +3 more
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Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models [PDF]
IEEE International Conference on Computer Vision, 2021 We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion ...
Zheyuan Liu +3 more
semanticscholar +1 more source
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Remote sensing (RS) cross-modal text–image retrieval has attracted extensive attention for its advantages of flexible input and efficient query. However, traditional methods ignore the characteristics of multiscale and redundant targets in RS image ...
Zhiqiang Yuan +6 more
semanticscholar +1 more source
Efficient and secure multi-user outsourced image retrieval scheme with access control
网络与信息安全学报, 2021 In the cloud-based image retrieval services, the cloud server may not be fully trusted in which may arise privacy concerns. Some privacy-preserving image retrieval schemes have been proposed to protect image privacy.
SONG Fuyuan, QIN Zheng, LIU Yu +1 more
doaj +3 more sources