12 citations · 21 across the 6 of their papers we have counts for
6 papers
Few-Shot Adaptation of Grounding DINO for Agricultural Domain
Rajhans Singh, Rafael Bidese Puhl, Kshitiz Dhakal +1
Deep learning models are transforming agricultural applications by enabling automated phenotyping, monitoring, and yield estimation. However, their effectiveness heavily depends on…
Polynomial Implicit Neural Representations For Large Diverse Datasets
Rajhans Singh, Ankita Shukla, Pavan Turaga
Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Mos…
Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments
Rajhans Singh, Ankita Shukla, Pavan Turaga
Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult…
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…
Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh, Pavan Turaga, Suren Jayasuriya +2
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However,…
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…