16 citations · 45 across the 15 of their papers we have counts for
16 papers
Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach
Vaishnavi Khindkar, Vineeth Balasubramanian, Chetan Arora +2
With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting ped…
CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads
Varun Gupta, Anbumani Subramanian, C. V. Jawahar +1
Unconstrained Asian roads often involve poor infrastructure, affecting overall road safety. Missing traffic signs are a regular part of such roads. Missing or non-existing object d…
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
Shubham Dokania, A. H. Abdul Hafez, Anbumani Subramanian +2
Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Pr…
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments
Shubham Dokania, Anbumani Subramanian, Manmohan Chandraker +1
High-quality structured data with rich annotations are critical components in intelligent vehicle systems dealing with road scenes. However, data curation and annotation require in…
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
Aman Goyal, Dev Agarwal, Anbumani Subramanian +3
In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involvin…
Automatic Quantification and Visualization of Street Trees
Arpit Bahety, Rohit Saluja, Ravi Kiran Sarvadevabhatla +2
Assessing the number of street trees is essential for evaluating urban greenery and can help municipalities employ solutions to identify tree-starved streets. It can also help iden…
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection
Ashutosh Agarwal, Anay Majee, Anbumani Subramanian +1
Few-shot object detection (FSOD) localizes and classifies objects in an image given only a few data samples. Recent trends in FSOD research show the adoption of metric and meta-lea…
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Anay Majee, Anbumani Subramanian, Kshitij Agrawal
Localization and recognition of less-occurring road objects have been a challenge in autonomous driving applications due to the scarcity of data samples. Few-Shot Object Detection…
Multi-Domain Incremental Learning for Semantic Segmentation
Prachi Garg, Rohit Saluja, Vineeth N Balasubramanian +3
Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment perfo…
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Anuj Tambwekar, Kshitij Agrawal, Anay Majee +1
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data,…
Few-Shot Learning for Road Object Detection
Anay Majee, Kshitij Agrawal, Anbumani Subramanian
Few-shot learning is a problem of high interest in the evolution of deep learning. In this work, we consider the problem of few-shot object detection (FSOD) in a real-world, class-…
Measures of Complexity for Large Scale Image Datasets
Ameet Annasaheb Rahane, Anbumani Subramanian
Large scale image datasets are a growing trend in the field of machine learning. However, it is hard to quantitatively understand or specify how various datasets compare to each ot…
Enhancing Object Detection in Adverse Conditions using Thermal Imaging
Kshitij Agrawal, Anbumani Subramanian
Autonomous driving relies on deriving understanding of objects and scenes through images. These images are often captured by sensors in the visible spectrum. For improved detection…
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Girish Varma, Anbumani Subramanian, Anoop Namboodiri +2
While several datasets for autonomous navigation have become available in recent years, they tend to focus on structured driving environments. This usually corresponds to well-deli…
One-Click Annotation with Guided Hierarchical Object Detection
Adithya Subramanian, Anbumani Subramanian
The increase in data collection has made data annotation an interesting and valuable task in the contemporary world. This paper presents a new methodology for quickly annotating da…
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
Ashish Mehta, Adithya Subramanian, Anbumani Subramanian
Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which…