Mrigank Rochan

Assistant Professor
Department of Computer Science
University of Saskatchewan

About Me

I am an Assistant Professor in the Department of Computer Science at the University of Saskatchewan. Previously, I was a Senior Researcher in the Autonomous Driving Perception team at Huawei Noah's Ark Lab, Canada. I received my Ph.D. and M.Sc. in Computer Science from the University of Manitoba in 2020 and 2016, respectively, where I was advised by Prof. Yang Wang. I obtained my B.Tech. degree in Computer Science and Engineering from Amrita University, India, in 2011. I was a visiting research student at Simon Fraser University in 2015-2016. During my Ph.D., I also did a research internship at Mapillary Research (acquired by Facebook).

My research is focused on Deep Learning and its applications in Computer Vision. For my thesis, I received the 2020 CIPPRS John Barron Doctoral Dissertation Award, a prestigious national award given annually by the Canadian Image Processing and Pattern Recognition Society (CIPPRS) to the top Ph.D. thesis in computer/robot vision.

News

^NEW 10/2024: One paper accepted to WACV 2025!
^NEW 10/2024: Two papers accepted to self-supervised learning workshop at NeurIPS 2024!
^NEW 07/2024: Received the Google PaliGemma Academic Program Award!
^NEW 06/2024: Awarded NSERC Discovery Grant!
^NEW 03/2024: Two papers accepted to IEEE IV 2024!
03/2023: One paper accepted to IEEE IV 2023!
03/2022: One paper accepted to CVPR 2022!
01/2022: One paper accepted to ICRA 2022!
07/2021: One paper accepted to ICCV 2021!
01/2021: 1 paper accepted to IEEE TPAMI!

Invited Talks

^NEW 10/2024: Talk at Adobe Research!
^NEW 10/2024: Talk at Microsoft Research!
12/2023: Talk at Google Research!
12/2023: Talk at Indian Institute of Science (IISc), Bangalore!

Teaching

CMPT 489/828: Deep Learning [Winter 2024]

Selected Publications

	Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence Zahidul Islam, Sujoy Paul, and Mrigank Rochan IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. 5th Workshop on Self-Supervised Learning: Theory and Practice (at NeurIPS), 2024. [arXiv]
	Test-Time Adaptation for Video Highlight Detection Zahidul Islam, Sujoy Paul, and Mrigank Rochan 5th Workshop on Self-Supervised Learning: Theory and Practice (at NeurIPS), 2024. [paper]
	Gradual Batch Alternation for Effective Domain Adaptation in LiDAR-Based 3D Object Detection Mrigank Rochan, Xingxin Chen, Alaap Grandhi, Eduardo Corral-Soto, and Bingbing Liu IEEE Intelligent Vehicles Symposium (IV), 2024. [paper]
	Effects of Range-based LiDAR Point Cloud Density Manipulation on 3D Object Detection Eduardo Corral-Soto, Alaap Grandhi, Yannis He, Mrigank Rochan, and Bingbing Liu IEEE Intelligent Vehicles Symposium (IV), 2024. [paper]
	Domain Adaptation in LiDAR Semantic Segmentation via Hybrid Learning With Alternating Skip Connections Eduardo Corral-Soto, Mrigank Rochan, Yannis He, Xingxin Chen, Shubhra Aich, and Bingbing Liu IEEE Intelligent Vehicles Symposium (IV), 2023. [paper]
	Contrastive Learning for Unsupervised Video Highlight Detection Taivanbat Badamdorj, Mrigank Rochan, Yang Wang, and Li Cheng IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. [paper]
	Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters Mrigank Rochan, Shubhra Aich, Eduardo Corral-Soto, Amir Nabatchian, and Bingbing Liu IEEE International Conference on Robotics and Automation (ICRA), 2022. [paper]
	Joint Visual and Audio Learning for Video Highlight Detection Taivanbat Badamdorj, Mrigank Rochan, Yang Wang, and Li Cheng IEEE/CVF International Conference on Computer Vision (ICCV), 2021. [paper]
	Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network Linwei Ye, Mrigank Rochan, Zhi Liu, Xiaoqin Zhang, and Yang Wang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. [arXiv] [code]
	AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting Mahesh Reddy, Mrigank Rochan, Yiwei Lu, and Yang Wang IEEE Transactions on Multimedia (TMM), 2021. [arXiv] [code]
	Efficient Deep Learning Models for Video Abstraction Mrigank Rochan Ph.D. Thesis, University of Manitoba, 2020. CIPPRS John Barron Doctoral Dissertation Award [announcement] [link]
	Adaptive Video Highlight Detection by Learning from User History Mrigank Rochan, Mahesh Reddy, Linwei Ye, and Yang Wang European Conference on Computer Vision (ECCV), 2020. [arXiv] [code]
	Sentence Guided Temporal Modulation for Dynamic Video Thumbnail Generation Mrigank Rochan, Mahesh Reddy, and Yang Wang British Machine Vision Conference (BMVC), 2020. [arXiv]
	Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning Mahesh Reddy, Md Hossain, Mrigank Rochan and Yang Wang IEEE Winter Conference of Applications on Computer Vision (WACV), 2020. [arXiv] [code]
	Video Summarization by Learning from Unpaired Data Mrigank Rochan and Yang Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [arXiv]
	Cross-Modal Self-Attention Network for Referring Image Segmentation Linwei Ye, Mrigank Rochan, Zhi Liu, and Yang Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [arXiv] [code]
	Convolutional Temporal Attention Model for Video-based Person Re-identification Tanzila Rahman, Mrigank Rochan, and Yang Wang IEEE International Conference on Multimedia and Expo (ICME), 2019. [arXiv]
	Video Summarization Using Fully Convolutional Sequence Networks Mrigank Rochan, Linwei Ye, and Yang Wang European Conference on Computer Vision (ECCV), 2018. [paper] [arXiv]
	Future Semantic Segmentation with Convolutional LSTM Shahab Nabavi, Mrigank Rochan, and Yang Wang British Machine Vision Conference (BMVC), 2018. [arXiv]
	Gated Feedback Refinement Network for Dense Image Labeling Md Amirul Islam, Mrigank Rochan, Neil Bruce, and Yang Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [paper] [extended version] [code]
	Person Re-Identification by Localizing Discriminative Regions Tanzila Rahman, Mrigank Rochan, and Yang Wang British Machine Vision Conference (BMVC), 2017. [paper]
	Adapting Object Detectors from Images to Weakly Labeled Videos Omit Chanda, Eu Wern Teh, Mrigank Rochan, Zhenyu Guo, and Yang Wang British Machine Vision Conference (BMVC), 2017. [paper] [code]
	Salient Object Detection using a Context-Aware Refinement Network Md Amirul Islam, Mahmoud Kalash, Mrigank Rochan, Neil Bruce, and Yang Wang British Machine Vision Conference (BMVC), 2017. [paper]
	Label Refinement Network for Coarse-to-Fine Semantic Segmentation Md Amirul Islam, Shujon Naha, Mrigank Rochan, Neil Bruce, and Yang Wang arXiv preprint arXiv:1606.07415, 2017. [arXiv]
	Attention Networks for Weakly Supervised Object Localization Eu Wern Teh, Mrigank Rochan, and Yang Wang British Machine Vision Conference (BMVC), 2016. [paper]
	Object Localization in Weakly Labeled Images and Videos Mrigank Rochan M.Sc. Thesis, University of Manitoba, 2016. [link]
	Weakly Supervised Object Localization and Segmentation in Videos Mrigank Rochan and Yang Wang Image and Vision Computing (IVC), 2016. [paper]
	Weakly Supervised Localization of Novel Objects using Appearance Transfer Mrigank Rochan and Yang Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [paper]
	Latent SVM for Object Localization in Weakly Labeled Videos Mrigank Rochan and Yang Wang Canadian Conference on Computer and Robot Vision (CRV), 2015. [paper]
	Efficient Object Localization and Segmentation in Weakly Labeled Videos Mrigank Rochan and Yang Wang International Symposium on Visual Computing (ISVC), 2014. Oral Presentation [paper]
	Segmenting Objects in Weakly Labeled Videos Mrigank Rochan, Shafin Rahman, Neil Bruce, and Yang Wang Canadian Conference on Computer and Robot Vision (CRV), 2014. Oral Presentation [paper] [demo video]
	Examining Visual Saliency Prediction in Naturalistic Scenes Shafin Rahman, Mrigank Rochan, Yang Wang, and Neil Bruce IEEE International Conference on Image Processing (ICIP), 2014. Oral Presentation [paper]
	See Google Scholar for full publication list