Search Results for author:
Found 9 papers, 1 papers with code
Semi-supervised Learning for Dense Object Detection in Retail Scenes
We show that using unlabeled data with the noisy student training methodology, we can improve the state of the art on precise detection of objects in densely packed retail scenes.
Commonsense Visual Sensemaking for Autonomous Driving: On Generalised Neurosymbolic Online Abduction Integrating Vision and Semantics
The method integrates state of the art in visual computing, and is developed as a modular framework that is generally usable within hybrid architectures for realtime perception and control.
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection
We train a standard object detector on a small, normally packed dataset with data augmentation techniques.
Ranked #1 on
Object Detection
on COCO 2017
(Mean mAP metric)
Out of Sight But Not Out of Mind: An Answer Set Programming Based Online Abduction Framework for Visual Sensemaking in Autonomous Driving
We demonstrate the need and potential of systematically integrated vision and semantics} solutions for visual sensemaking (in the backdrop of autonomous driving).
Multidomain Document Layout Understanding using Few Shot Object Detection
We try to address the problem of document layout understanding using a simple algorithm which generalizes across multiple domains while training on just few examples per domain.
Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset
We enhance the FCN output mask into final output bounding boxes by a Convolutional Encoder-Decoder (ConvAE) viz.
Detection of Tooth caries in Bitewing Radiographs using Deep Learning
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries.
Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks
This work is an endeavor to develop a deep learning methodology for automated anatomical labeling of a given region of interest (ROI) in brain computed tomography (CT) scans.
RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans
Further, the model utilizes 3D context from neighboring slices to improve predictions at each slice and subsequently, aggregates the slice-level predictions to provide diagnosis at CT level.
