Tata Consultancy Services Limited

Anima Majumder

Chandan Kumar Singh

Swagat Kumar

Rajesh Kumar Sinha

Prakash Chanderlal Ambwani

Laxmidhar Behera

Vivek Kumar Gangwar

Sinha Rajesh

This disclosure relates generally to system and method for parameter compression of capsule networks using deep features. The conventional capsule networks have distinct capability of retaining spatial correlations between extracted features but that comes at a cost of intensive computational, cost, memory usage and bandwidth requirement. The embodiments herein disclose a system and method for employing a lightweight deep features based capsule network that is capable of compressing the parameters. In an embodiment, the system includes a deep feature based capsule network such that the capsule layer is preceded by feature blocks. Said feature blocks comprises convolutional operation with a kernel size 3, followed by convolutional operation with kernel of size 1, and a Batch Normalization layer, and hence are able to extract deep features. These deep features when input to the capsule layer facilitates in compressing the parameters of the capsule layer, while ensuring significant increase in recognition accuracy.

System and method for parameter compression of capsule networks using deep features

Object annotation is images is tedious time consuming task when large volume of data needs to annotated. Existing methods limit to semiautomatic approaches for annotation. The embodiments herein provide a method and system for a deep network based architecture for automatic object annotation. The deep network utilized is a two stage network with first stage as an annotation model comprising a Faster Region-based Fully Convolutional Networks (F-RCNN) and Region-based Fully Convolutional Networks (RFCN) providing for two class classification to generate annotated images from a set of single object test images. Further, the newly annotated test object images are then used to synthetically generate cluttered images and their corresponding annotations, which are used to train the second stage of the deep network comprising the multi-class object detection/classification model designed using the F-RCNN and the RFCN as base networks to automatically annotate input test image in real time.

Method and system for automatic object annotation using deep network

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Bengaluru, India

Anima Majumder