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pro vyhledávání: '"Srivastava, Muktabh Mayank"'
Autor:
Srivastava, Muktabh Mayank
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 evaluation. Previous works explore ways to finetune deep models for thi
Externí odkaz:
http://arxiv.org/abs/2312.10282
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 detect products on retail shelves and then classify them. However, there ar
Externí odkaz:
http://arxiv.org/abs/2110.03783
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 of companies' retail marketing strategy and are often applied as styliz
Externí odkaz:
http://arxiv.org/abs/2110.03646
Autor:
Srivastava, Muktabh Mayank
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 dog or tree could have. Previous works have shown different methods t
Externí odkaz:
http://arxiv.org/abs/2110.03639
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 limited ability to annotate data from across domains. We show that given a
Externí odkaz:
http://arxiv.org/abs/2110.02200
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
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 BERT can encode not only just the words in a document but also the contex
Externí odkaz:
http://arxiv.org/abs/2011.11673
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 on-device, a compact efficient network becomes inevitable. In this pa
Externí odkaz:
http://arxiv.org/abs/2004.13094
Autor:
Srivastava, Muktabh Mayank
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 execution evaluation. In this work, we present various tricks to increase
Externí odkaz:
http://arxiv.org/abs/2001.03992
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