40 citations · 58 across the 11 of their papers we have counts for
14 papers
Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images
Hugo Bertiche, Niloy J. Mitra, Kuldeep Kulkarni +5
Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs…
Self-supervised Multi-view Disentanglement for Expansion of Visual Collections
Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari +2
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 o…
GEMS: Scene Expansion using Generative Models of Graphs
Rishi Agarwal, Tirupati Saketh Chandra, Vaidehi Patil +3
Applications based on image retrieval require editing and associating in intermediate spaces that are representative of the high-level concepts like objects and their relationships…
Controllable Animation of Fluid Elements in Still Images
Aniruddha Mahapatra, Kuldeep Kulkarni
We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements lik…
SemIE: Semantically-aware Image Extrapolation
Bholeshwar Khurana, Soumya Ranjan Dash, Abhishek Bhatia +3
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 capabilit…
Halluci-Net: Scene Completion by Exploiting Object Co-occurrence Relationships
Kuldeep Kulkarni, Tejas Gokhale, Rajhans Singh +2
Recently, there has been substantial progress in image synthesis from semantic labelmaps. However, methods used for this task assume the availability of complete and unambiguous la…
Rate-Adaptive Neural Networks for Spatial Multiplexers
Suhas Lohit, Rajhans Singh, Kuldeep Kulkarni +1
In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high…
CS-VQA: Visual Question Answering with Compressively Sensed Images
Li-Chi Huang, Kuldeep Kulkarni, Anik Jha +3
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…
Compressive Light Field Reconstructions using Deep Learning
Mayank Gupta, Arjun Jauhari, Kuldeep Kulkarni +3
Light field imaging is limited in its computational processing demands of high sampling for both spatial and angular dimensions. Single-shot light field cameras sacrifice spatial r…
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images
Suhas Lohit, Kuldeep Kulkarni, Ronan Kerviche +2
Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this work, we propose a data driven non-itera…
Weakly Supervised Learning of Heterogeneous Concepts in Videos
Sohil Shah, Kuldeep Kulkarni, Arijit Biswas +3
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. The c…
Fast Integral Image Estimation at 1% measurement rate
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 recon…
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
Kuldeep Kulkarni, Suhas Lohit, Pavan Turaga +2
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 t…
Reconstruction-free action inference from compressive imagers
Kuldeep Kulkarni, Pavan Turaga
Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference i…