33 citations · 33 across the 9 of their papers we have counts for
10 papers · 1 filter
PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction
Naman Mishra, Shankar Gangisetty, C. V. Jawahar
Pedestrian intention and trajectory prediction are critical for the safe deployment of autonomous driving systems, directly influencing navigation decisions in complex traffic envi…
MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding
Varun A. Paturkar, Shankar Gangisetty, C. V. Jawahar
Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags far behind four-wheelers, whe…
DriveSafe: A Framework for Risk Detection and Safety Suggestions in Driving Scenarios
Sainithin Artham, Shankar Gangisetty, Avijit Dasgupta +1
Comprehensive situational awareness is essential for autonomous vehicles operating in safety-critical environments, as it enables the identification and mitigation of potential ris…
Towards Safer and Understandable Driver Intention Prediction
Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai +2
Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomo…
Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD
Ruthvik Bokkasam, Shankar Gangisetty, A. H. Abdul Hafez +1
With the rapid advancements in autonomous driving, accurately predicting pedestrian behavior has become essential for ensuring safety in complex and unpredictable traffic condition…
ICPR 2024 Competition on Rider Intention Prediction
Shankar Gangisetty, Abdul Wasi, Shyam Nandan Rai +8
The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly f…
Making the V in Text-VQA Matter
Shamanthak Hegde, Soumya Jahagirdar, Shankar Gangisetty
Text-based VQA aims at answering questions by reading the text present in the images. It requires a large amount of scene-text relationship understanding compared to the VQA task.…
Weakly Supervised Visual Question Answer Generation
Charani Alampalle, Shamanthak Hegde, Soumya Jahagirdar +1
Growing interest in conversational agents promote twoway human-computer communications involving asking and answering visual questions have become an active area of research in AI.…
Look, Read and Ask: Learning to Ask Questions by Reading Text in Images
Soumya Jahagirdar, Shankar Gangisetty, Anand Mishra
We present a novel problem of text-based visual question generation or TextVQG in short. Given the recent growing interest of the document image analysis community in combining tex…
PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation
Sindhu Hegde, Shankar Gangisetty
Point clouds, being the simple and compact representation of surface geometry of 3D objects, have gained increasing popularity with the evolution of deep learning networks for clas…