Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Kuldeep Kulkarni"'
Image search engines enable the retrieval of images relevant to a query image. In this work, we consider the setting where a query for similar images is derived from a collection of images. For visual search, the similarity measurements may be made a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::208c5df99f4d58317556cbca5c55adac
http://arxiv.org/abs/2302.02249
http://arxiv.org/abs/2302.02249
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Autor:
Bholeshwar Khurana, Soumya Ranjan Dash, Abhishek Bhatia, Aniruddha Mahapatra, Hrituraj Singh, Kuldeep Kulkarni
We propose a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. All previous methods are limited in their capability of extrapolation to merely extending the already existing objects in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e007a8ca728aeb2529177cc92c1e2e6
http://arxiv.org/abs/2108.13702
http://arxiv.org/abs/2108.13702
Publikováno v:
IEEE Transactions on Computational Imaging. 4:326-340
Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a data-driven noniterative algorithm to overcome the shortcomings of earlier iterative algor
Publikováno v:
ACSSC
Compressive imaging is used to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference with purely data-driven models using deep learning. Although random projection has some advantages, we can
Publikováno v:
ICIP
Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop
Publikováno v:
ICIP
Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small n
Publikováno v:
CVPR
The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) archite
Publikováno v:
Computer Vision – ECCV 2016 ISBN: 9783319464657
ECCV (6)
ECCV (6)
Typical textual descriptions that accompany online videos are ‘weak’: i.e., they mention the important heterogeneous concepts in the video but not their corresponding spatio-temporal locations. However, certain location constraints on these conce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b02546c80ecd958deee8838d93ac72ff
https://doi.org/10.1007/978-3-319-46466-4_17
https://doi.org/10.1007/978-3-319-46466-4_17