75 citations · 144 across the 19 of their papers we have counts for
24 papers
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…
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…
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…
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…
Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets
Natesh Reddy, Muktabh Mayank Srivastava
The problem statement addressed in this work is : For a public sentiment classification API, how can we set up a classifier that works well on different types of data, having limit…
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…
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
Natesh Reddy, Pranaydeep Singh, Muktabh Mayank Srivastava
When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment. Massively pretrained language models like BER…
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…
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…
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…
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
Harshita Seth, Pulkit Kumar, Muktabh Mayank Srivastava
Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data…
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…
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…
Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection
Nishant Nikhil, Muktabh Mayank Srivastava
In this paper, we describe the system submitted for the SemEval 2018 Task 3 (Irony detection in English tweets) Subtask A by the team Binarizer. Irony detection is a key task for m…
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…
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…
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.…
Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
Pushpankar Kumar Pushp, Muktabh Mayank Srivastava
Zero-shot Learners are models capable of predicting unseen classes. In this work, we propose a Zero-shot Learning approach for text categorization. Our method involves training mod…
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…
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…
Content Based Document Recommender using Deep Learning
Nishant Nikhil, Muktabh Mayank Srivastava
With the recent advancements in information technology there has been a huge surge in amount of data available. But information retrieval technology has not been able to keep up wi…
Testing the limits of unsupervised learning for semantic similarity
Richa Sharma, Muktabh Mayank Srivastava
Semantic Similarity between two sentences can be defined as a way to determine how related or unrelated two sentences are. The task of Semantic Similarity in terms of distributed r…
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…
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…