activity
20172024
most citedTrain Once, Test Anywhere: Zero-Shot Learning for Text Classification

75 citations · 144 across the 19 of their papers we have counts for

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17 papers · 1 filter

cs.CV2024

RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification

Muktabh Mayank Srivastava

Retail product or packaged grocery goods images need to classified in various computer vision applications like self checkout stores, supply chain automation and retail execution e…

cs.CV2021

Machine Learning approaches to do size based reasoning on Retail Shelf objects to classify product variants

Muktabh Mayank Srivastava, Pratyush Kumar

There has been a surge in the number of Machine Learning methods to analyze products kept on retail shelves images. Deep learning based computer vision methods can be used to detec…

cs.CV2021

Using Keypoint Matching and Interactive Self Attention Network to verify Retail POSMs

Harshita Seth, Sonaal Kant, Muktabh Mayank Srivastava

Point of Sale Materials(POSM) are the merchandising and decoration items that are used by companies to communicate product information and offers in retail stores. POSMs are part o…

cs.CV2021

Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification

Muktabh Mayank Srivastava

Retail product Image classification problems are often few shot classification problems, given retail product classes cannot have the type of variations across images like a cat or…

cs.CV2021

Semi-supervised Learning for Dense Object Detection in Retail Scenes

Jaydeep Chauhan, Srikrishna Varadarajan, Muktabh Mayank Srivastava

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 co…

cs.CV2020

Compact retail shelf segmentation for mobile deployment

Pratyush Kumar, Muktabh Mayank Srivastava

The recent surge of automation in the retail industries has rapidly increased demand for applying deep learning models on mobile devices. To make the deep learning models real-time…

cs.CV2020

Bag of Tricks for Retail Product Image Classification

Muktabh Mayank Srivastava

Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail exec…

cs.CV2020

Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

Srikrishna Varadarajan, Sonaal Kant, Muktabh Mayank Srivastava

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 dataset…

cs.CV2018★ 2 cited

Multidomain Document Layout Understanding using Few Shot Object Detection

Pranaydeep Singh, Srikrishna Varadarajan, Ankit Narayan Singh +1

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…

cs.CV2018

Example Mining for Incremental Learning in Medical Imaging

Pratyush Kumar, Muktabh Mayank Srivastava

Incremental Learning is well known machine learning approach wherein the weights of the learned model are dynamically and gradually updated to generalize on new unseen data without…

cs.CV2018

Weakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset

Srikrishna Varadarajan, Muktabh Mayank Srivastava

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 Convolutio…

cs.CV2018★ 3 cited

Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning

Muktabh Mayank Srivastava, Sonaal Kant

We train a deep Convolutional Neural Network (CNN) from scratch for visual aesthetic analysis in images and discuss techniques we adopt to improve the accuracy. We avoid the preval…

cs.CV2018

Towards Automated Tuberculosis detection using Deep Learning

Sonaal Kant, Muktabh Mayank Srivastava

Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to 480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost US$340 Billion due to TB.…

cs.CV2017

Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

Pulkit Kumar, Monika Grewal, Muktabh Mayank Srivastava

Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X…

cs.CV2017★ 61 cited

Detection of Tooth caries in Bitewing Radiographs using Deep Learning

Muktabh Mayank Srivastava, Pratyush Kumar, Lalit Pradhan +1

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…

cs.CV2017

Anatomical labeling of brain CT scan anomalies using multi-context nearest neighbor relation networks

Srikrishna Varadarajan, Muktabh Mayank Srivastava, Monika Grewal +1

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 co…

cs.CV2017

RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scans

Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar +1

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 anal…