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Found 24 papers, 4 papers with code
RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification
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.
Using Keypoint Matching and Interactive Self Attention Network to verify Retail POSMs
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.
Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification
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.
Machine Learning approaches to do size based reasoning on Retail Shelf objects to classify product variants
There has been a surge in the number of Machine Learning methods to analyze products kept on retail shelves images.
Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets
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.
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.
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
When performing Polarity Detection for different words in a sentence, we need to look at the words around to understand the sentiment.
Ranked #10 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(Restaurant (Acc) metric)
Aspect-Based Sentiment Analysis (ABSA)
Language Modelling
+2
Compact retail shelf segmentation for mobile deployment
In this paper, we work on one such common problem in the retail industries - Shelf segmentation.
Bag of Tricks for Retail Product Image Classification
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.
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)
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data
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
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.
Example Mining for Incremental Learning in Medical Imaging
Incremental learning proves to be time as well as resource-efficient solution for deployment of deep learning algorithms in real world as the model can automatically and dynamically adapt to new data as and when annotated data becomes available.
Binarizer at SemEval-2018 Task 3: Parsing dependency and deep learning for irony detection
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.
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.
Towards Automated Tuberculosis detection using Deep Learning
Tuberculosis(TB) in India is the world's largest TB epidemic.
Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
Our method involves training model on a large corpus of sentences to learn the relationship between a sentence and embedding of sentence's tags.
Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning
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.
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases.
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.
Content Based Document Recommender using Deep Learning
But information retrieval technology has not been able to keep up with this pace of information generation resulting in over spending of time for retrieving relevant information.
Testing the limits of unsupervised learning for semantic similarity
The task of Semantic Similarity in terms of distributed representations can be thought to be generating sentence embeddings (dense vectors) which take both context and meaning of sentence in account.
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.
