Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Varadarajan, Srikrishna"'
Retail scenes usually contain densely packed high number of objects in each image. Standard object detection techniques use fully supervised training methodology. This is highly costly as annotating a large dense retail object detection dataset invol
Externí odkaz:
http://arxiv.org/abs/2107.02114
We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking in the backdrop of autonomous driving. A general neurosymbolic method for online visual sensemaking using answer set programming
Externí odkaz:
http://arxiv.org/abs/2012.14359
Object detection in densely packed scenes is a new area where standard object detectors fail to train well. Dense object detectors like RetinaNet trained on large and dense datasets show great performance. We train a standard object detector on a sma
Externí odkaz:
http://arxiv.org/abs/1912.09476
We demonstrate the need and potential of systematically integrated vision and semantics} solutions for visual sensemaking (in the backdrop of autonomous driving). A general method for online visual sensemaking using answer set programming is systemat
Externí odkaz:
http://arxiv.org/abs/1906.00107
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. We approach this problem via supervised object detection method and prop
Externí odkaz:
http://arxiv.org/abs/1808.07330
We propose a weakly supervised method using two algorithms to predict object bounding boxes given only an image classification dataset. First algorithm is a simple Fully Convolutional Network (FCN) trained to classify object instances. We use the pro
Externí odkaz:
http://arxiv.org/abs/1803.06813
We develop a Computer Aided Diagnosis (CAD) system, which enhances the performance of dentists in detecting wide range of dental caries. The CAD System achieves this by acting as a second opinion for the dentists with way higher sensitivity on the ta
Externí odkaz:
http://arxiv.org/abs/1711.07312
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. We combine both local and global context to obtain a representation of the
Externí odkaz:
http://arxiv.org/abs/1710.09180
We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sif
Externí odkaz:
http://arxiv.org/abs/1710.04934
Publikováno v:
In Artificial Intelligence October 2021 299
