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

10/07/2021
by   Harshita Seth, et al.
0

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 stylized window displays around retail shelves. In this work, we apply computer vision techniques to the task of verification of POSMs in supermarkets by telling if all desired components of window display are present in a shelf image. We use Convolutional Neural Network based unsupervised keypoint matching as a baseline to verify POSM components and propose a supervised Neural Network based method to enhance the accuracy of baseline by a large margin. We also show that the supervised pipeline is not restricted to the POSM material it is trained on and can generalize. We train and evaluate our model on a private dataset composed of retail shelf images.

READ FULL TEXT
research
05/27/2022

Image Keypoint Matching using Graph Neural Networks

Image matching is a key component of many tasks in computer vision and i...
research
02/27/2021

FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images

Keypoint detection and description is a commonly used building block in ...
research
01/18/2023

OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models

We propose a new method for object pose estimation without CAD models. T...
research
10/31/2018

Convolutional Self-Attention Network

Self-attention network (SAN) has recently attracted increasing interest ...
research
03/29/2017

Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation

We motivate and address a human-in-the-loop variant of the monocular vie...
research
07/18/2023

Unsupervised Deep Graph Matching Based on Cycle Consistency

We contribute to the sparsely populated area of unsupervised deep graph ...

Please sign up or login with your details

Forgot password? Click here to reset