no code implementations • CVPR 2023 • Hugo Bertiche, Niloy J. Mitra, Kuldeep Kulkarni, Chun-Hao Paul Huang, Tuanfeng Y. Wang, Meysam Madadi, Sergio Escalera, Duygu Ceylan
We investigate the problem in the context of dressed humans under the wind.
no code implementations • 4 Feb 2023 • Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni
In this work, we consider the setting where a query for similar images is derived from a collection of images.
no code implementations • 8 Jul 2022 • Rishi Agarwal, Tirupati Saketh Chandra, Vaidehi Patil, Aniruddha Mahapatra, Kuldeep Kulkarni, Vishwa Vinay
To this end, we formulate scene graph expansion as a sequential prediction task involving multiple steps of first predicting a new node and then predicting the set of relationships between the newly predicted node and previous nodes in the graph.
no code implementations • CVPR 2022 • Aniruddha Mahapatra, Kuldeep Kulkarni
The user-provided input arrow directions, their corresponding speed values, and the mask are then converted into a dense flow map representing a constant optical flow map (FD).
no code implementations • ICCV 2021 • Bholeshwar Khurana, Soumya Ranjan Dash, Abhishek Bhatia, Aniruddha Mahapatra, Hrituraj Singh, Kuldeep Kulkarni
The, thus, obtained segmentation map is fed into a network to compute the extrapolated semantic segmentation and the corresponding panoptic segmentation maps.
no code implementations • 18 Apr 2020 • Kuldeep Kulkarni, Tejas Gokhale, Rajhans Singh, Pavan Turaga, Aswin Sankaranarayanan
The generated dense labelmap can then be used as input by state-of-the-art image synthesis techniques like pix2pixHD to obtain the final image.
no code implementations • 8 Sep 2018 • Suhas Lohit, Rajhans Singh, Kuldeep Kulkarni, Pavan Turaga
Using standard datasets, we demonstrate that, when tested over a range of MRs, a rate-adaptive network can provide high quality reconstruction over a the entire range, resulting in up to about 15 dB improvement over previous methods, where the network is valid for only one MR. We demonstrate the effectiveness of our approach for sample-efficient object tracking where video frames are acquired at dynamically varying MRs. We also extend this algorithm to learn the measurement operator in conjunction with image recognition networks.
no code implementations • 8 Jun 2018 • Li-Chi Huang, Kuldeep Kulkarni, Anik Jha, Suhas Lohit, Suren Jayasuriya, Pavan Turaga
Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition.
no code implementations • 5 Feb 2018 • Mayank Gupta, Arjun Jauhari, Kuldeep Kulkarni, Suren Jayasuriya, Alyosha Molnar, Pavan Turaga
We test our network reconstructions on synthetic light fields, simulated coded measurements of real light fields captured from a Lytro Illum camera, and real coded images from a custom CMOS diffractive light field camera.
no code implementations • 15 Aug 2017 • Suhas Lohit, Kuldeep Kulkarni, Ronan Kerviche, Pavan Turaga, Amit Ashok
We show empirically that our algorithm yields reconstructions with higher PSNRs compared to iterative algorithms at low measurement rates and in presence of measurement noise.
no code implementations • 12 Jul 2016 • Sohil Shah, Kuldeep Kulkarni, Arijit Biswas, Ankit Gandhi, Om Deshmukh, Larry Davis
Typical textual descriptions that accompany online videos are 'weak': i. e., they mention the main concepts in the video but not their corresponding spatio-temporal locations.
no code implementations • CVPR 2016 • Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, Amit Ashok
The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image.
no code implementations • 27 Jan 2016 • Kuldeep Kulkarni, Pavan Turaga
We propose a framework called ReFInE to directly obtain integral image estimates from a very small number of spatially multiplexed measurements of the scene without iterative reconstruction of any auxiliary image, and demonstrate their practical utility in visual object tracking.
1 code implementation • CVPR 2016 • Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga, Ronan Kerviche, Amit Ashok
The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image.
no code implementations • 18 Jan 2015 • Kuldeep Kulkarni, Pavan Turaga
In this paper, we propose reconstruction-free methods for action recognition from compressive cameras at high compression ratios of 100 and above.