5 papers
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…