Perla Sai Raj Kishore

Learning to teach machines how to learn.

I am a Master's student at the School of Computing Science, Simon Fraser University (SFU).

Before joining SFU, I worked as a Senior Research Engineer at Staqu Technologies, where I designed and developed systems that revolved around Computer Vision and Deep Learning. And even earlier, during my bachelor's, I worked with Prof. Ujjwal Bhattacharya of Indian Statistical Institute (ISI), Kolkata and Prof. Partha Pratim Roy of Indian Institute of Technology (IIT) Roorkee on a variety of Computer Vision research problems.

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Research Interests

I am interested in Computer Vision, Computer Graphics, and Deep Learning in general. Particularly, understanding the world around from 2D & 3D visual data through systems that can effectively utilize the acquired knowledge and data from other similar tasks and domains, learn from data with limited or no labels, and are robust in diverse real-world scenarios.

An End-To-End Framework For Pose Estimation of Occluded Pedestrians
Sudip Das*, Perla Sai Raj Kishore*, Ujjwal Bhattacharya
International Conference on Image Processing (ICIP), 2020

Abstract / BibTex

ClueNet: A Deep Framework for Occluded Pedestrian Pose Estimation
Perla Sai Raj Kishore*, Sudip Das*, Partha Sarathi Mukherjee, Ujjwal Bhattacharya
British Machine Vision Conference (BMVC), 2019

Abstract / BibTex

Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning
Ayan Kumar Bhunia, Abhirup Das, Ankan Kumar Bhunia, Perla Sai Raj Kishore, Partha Pratim Roy
Conference on Computer Vision and Pattern Recognition (CVPR), 2019

Abstract / Code / arXiv / BibTex

User Constrained Thumbnail Generation Using Adaptive Convolutions
Perla Sai Raj Kishore, Ayan Kumar Bhunia, Shovozit Ghose, Partha Pratim Roy
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019

Abstract / Code / arXiv / BibTex

Texture Synthesis Guided Deep Hashing for Texture Image Retrieval
Ayan Kumar Bhunia, Perla Sai Raj Kishore, Pranay Mukherjee, Abhirup Das, Partha Pratim Roy
Winter Conference on Applications of Computer Vision (WACV), 2019

Abstract / arXiv / BibTex

Flatten-T Swish: A thresholded ReLU-Swish-like Activation Function for Deep Learning
Hock Hung Chieng, Noorhaniza Wahid, Ong Pauline, Perla Sai Raj Kishore
International Journal of Advances in Intelligent Informatics (IJAIN), 2018  
(Best Paper Award)

Abstract / Code / arXiv / BibTex

Saliency Detection: PyTorch implementation of a CVPR 2019 Publication

PyTorch implementation of the paper "Pyramid Feature Attention Network for Saliency Detection", published at CVPR 2019.

Code / Paper

Single Image Super Resolution

Image Super Resolution aims to increase the resolution of an image by generating pixels that interpolate best between a given low resolution and the required high resolution image. I built a deep learning based model for this purpose. A large amount of diverse data was also collected to train this model. The model was implemented using Keras in Python and comes with an easy to use graphical user interface. This was my project as an intern under Prof. A. V. Subramanyam of IIIT, Delhi.


Mixture Density Networks

Mixture Density Networks (MDNs) are an interesting way to address multimodality (where the input and output hold a one-to-many relationship). In such scenarios, instead of directly predicting the output we model the probability distribution of the output as a weighed mixture of several Gaussians from which we sample the actual output. In this project, I implemented univariate and bivariate MDNs in Python using Tensorflow.

Code / Original Paper

Character Level Language Model

Auto-correct and auto-complete, which have now become a standard feature in almost all virtual keyboards, make use of a language model at its core. In this project, I built an LSTM based character-level language model that aims to predict the next character from a sequence of input characters. The code for this project was written in Python using Tensorflow.


Lane Detection in NFS: Underground 2

Self Driving Cars are one of the fascinating technologies in this modern world. Though the entire process, from perceiving the surroundings to getting the car to move, is fairly complex, the first step usually begins with the detection of lanes that guide the vehicle on the road. In this project, I attempt to detect lanes in real-time in one of the popular games, "NFS: Underground 2", using OpenCV in Python.


Machine Learning Algorithms

In this project, I implemented various Machine Learning algorithms from scratch in Python using only Numpy.


Template credits : Dr. Jon Barron