activity
20152023
most citedFreehand Sketch Recognition Using Deep Features

28 citations · 49 across the 13 of their papers we have counts for

collaborators

18 papers

cs.CV2023

Automated Detection and Counting of Windows using UAV Imagery based Remote Sensing

Dhruv Patel, Shivani Chepuri, Sarvesh Thakur +3

Despite the technological advancements in the construction and surveying sector, the inspection of salient features like windows in an under-construction or existing building is pr…

cs.CV2022

Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches

Sravya Vardhani Shivapuja, Ashwin Gopinath, Ayush Gupta +2

The data distribution in popular crowd counting datasets is typically heavy tailed and discontinuous. This skew affects all stages within the pipelines of deep crowd counting appro…

cs.CV2021

MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion

Shubh Maheshwari, Debtanu Gupta, Ravi Kiran Sarvadevabhatla

We introduce MUGL, a novel deep neural model for large-scale, diverse generation of single and multi-person pose-based action sequences with locomotion. Our controllable approach e…

cs.CV2021

Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts

Prema Satish Sharan, Sowmya Aitha, Amandeep Kumar +3

Handwritten documents are often characterized by dense and uneven layout. Despite advances, standard deep network based approaches for semantic layout segmentation are not robust t…

cs.CV2021

BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation

Abhishek Trivedi, Ravi Kiran Sarvadevabhatla

Precise boundary annotations of image regions can be crucial for downstream applications which rely on region-class semantics. Some document collections contain densely laid out, h…

cs.CV2021

Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting

Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj +2

Datasets for training crowd counting deep networks are typically heavy-tailed in count distribution and exhibit discontinuities across the count range. As a result, the de facto st…