Search Results for author: Kuldeep Kulkarni

Found 15 papers, 1 papers with code

GEMS: Scene Expansion using Generative Models of Graphs

no code implementations8 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.

Graph Generation Image Retrieval +1

Controllable Animation of Fluid Elements in Still Images

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).

Generative Adversarial Network Optical Flow Estimation

SemIE: Semantically-aware Image Extrapolation

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.

Object Panoptic Segmentation +1

Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships

no code implementations18 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.

Image Generation Semantic Segmentation

Rate-Adaptive Neural Networks for Spatial Multiplexers

no code implementations8 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.

Object Tracking valid

CS-VQA: Visual Question Answering with Compressively Sensed Images

no code implementations8 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.

Question Answering Visual Question Answering

Compressive Light Field Reconstructions using Deep Learning

no code implementations5 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.

Compressive Sensing

Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images

no code implementations15 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.

Compressive Sensing Object Tracking

Weakly Supervised Learning of Heterogeneous Concepts in Videos

no code implementations12 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.

General Classification Weakly-supervised Learning

Fast Integral Image Estimation at 1% measurement rate

no code implementations27 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.

Visual Object Tracking

Reconstruction-free action inference from compressive imagers

no code implementations18 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.

Action Recognition Compressive Sensing +1

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