Search Results for author: Rajhans Singh

Found 6 papers, 1 papers with code

Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks

no code implementations22 Mar 2024 Sudhir Sornapudi, Rajhans Singh

Computer vision in agriculture is game-changing with its ability to transform farming into a data-driven, precise, and sustainable industry.

Contrastive Learning Representation Learning +1

Polynomial Implicit Neural Representations For Large Diverse Datasets

1 code implementation CVPR 2023 Rajhans Singh, Ankita Shukla, Pavan Turaga

With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains.

Conditional Image Generation

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

Non-Parametric Priors For Generative Adversarial Networks

no code implementations16 May 2019 Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging.

Data Augmentation Image Generation

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

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