Himangi Mittal

I am a first year Ph.D. student in the Robotics Institute (RI) at Carnegie Mellon University (CMU), working with Prof. Shubham Tulsiani. I graduated with a Master of Science in Robotics (MSR) from the Robotics Institute (RI) at Carnegie Mellon University (CMU) where I worked with Prof. Abhinav Gupta and collaborated with Prof. Pedro Morgado at UW-Madison. Previously, I worked as a Research Assistant at CMU with Prof. David Held at the R-Pad Lab, in collaboration with Pittsburgh-based autonomous driving company, Argo AI.

During my Masters at CMU, I am working on self-supervised representation learning methods for multimodal audio-visual videos. Previously, as a RA at CMU, I worked on self-supervised algorithms for 3D LiDAR point clouds. I did my bachelors from Jaypee Institute of Information Technology, Noida, India where I worked with Dr. Anuja Arora.

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  • August 2023 : Started my Ph.D. in the Robotics Institute (RI) at Carnegie Mellon University (CMU).
  • May 2023 : Started research internship at Honda Research Institute (HRI), San Jose, California.
  • Jan 2023 : Teaching Assistant for 16-825: Learning for 3D Vision.
  • Sep 2022 : Paper accepted at NeurIPS 2022.
  • Oct 2021 : Paper accepted at BMVC 2021 (Oral).
  • Apr 2021 - Dec 2021: I will be serving as a reviewer for ICCV 2021, AAAI 2022, WACV 2022, and CVPR 2022.
  • Aug 2021: Journal paper accepted in PAA (in collaboration with Robert Bosch, India).
  • Feb 2021: Accepted as a Master of Science in Robotics (MSR) student at Carnegie Mellon University for Fall 2021.
  • July 2020: Presented a short paper at RSS Workshop on Self-Supervised Robot Learning 2020.
  • Feb 2020: Paper accepted at CVPR 2020 (Oral).

I am interested in self-supervised learning, multi-modal machine learning, video representation learning, generative models, point clouds and autonomous driving.

Learning State-Aware Visual Representations from Audible Interactions
Himangi Mittal, Pedro Morgado, Unnat Jain, Abhinav Gupta
[NeurIPS 2022]
ECCV 2022 Workshop on Visual Object-oriented Learning meets Interaction (VOLI): Discovery, Representations, and Applications
Sight and Sound Workshop (CVPR 2023)
Arxiv / Code / Video

We propose a self-supervised algorithm to learn representations from untrimmed, egocentric videos containing audible interactions. Our method uses the audio signals in two unique ways: (1) to identify moments in time that are conducive to better self-supervised learning and (2) to learn representations that focus on the visual state changes caused by audible interactions.

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Self-Supervised Point Cloud Completion via Inpainting
Himangi Mittal, Brian Okorn, Arpit Jangid, David Held
[BMVC 2021 - Oral (Selection rate 3.3%)]
Paper / Arxiv / Code / Conference Presentation / Webpage

A self-supervised method to complete the incomplete, partial point clouds for real-world settings like LiDAR where ground truth complete point cloud annotations are unavailable. We achieve this via inpainting where a region of the point cloud is removed and the network is trained to complete this removed region.

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Harnessing emotions for depression detection
Sahana Prabhu Muraleedhara Himangi Mittal, Rajesh Varagani, Sweccha Jha, Shivendra Singh
[Pattern Analysis and Applications Journal]

A method for multi-modal depression detection using audio, video, and textual modalities using LSTMs. This work leverages emotions to detect an early indication of depression.

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Just Go with the Flow: Self-Supervised Scene Flow Estimation
Himangi Mittal, Brian Okorn, David Held
[CVPR 2020 - Oral (Selection rate 5.7%)]
RSS 2020 Workshop on Self-Supervised Robot Learning
Paper / Arxiv / Code / Project Page / Video / Short Paper

A method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets.

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Interpreting Context of Images using Scene Graphs
Himangi Mittal, Ajith Abraham, Anuja Arora
[International Conference on Big Data Analytics (BDA), 2019]
Paper / ArXiv / Code

Predicted action and spatial relationships in images between objects detected by YOLO, then combining VGG-Net based visual features and Word2Vec based semantic features.

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Anomaly Detection using Graph Neural Networks
Anshika Chaudhary, Himangi Mittal, Anuja Arora
[International Conference on Machine Learning, Big Data, Cloud and Parallel Computing , 2019]
Paper / Code

A method to capture the anomalous behavior in a social network based on degree, betweenness, and closeness of graph nodes using Graph Neural Networks (GNN) in Keras.

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STWalk: Learning Trajectory Representations in Temporal Graphs
Supriya Pandhre, Himangi Mittal Manish Gupta, Vineeth N. Balasubramanian
[ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD), 2018]
Paper / ArXiv / Code

Presents trajectory analysis of spatio-temporal graph nodes using DeepWalk algorithm in NetworkX (Python) for classification and detecting changing points of interest using SVMs.

Academic Service/Volunteer Work
  • Teaching Assistant: 16-824: Visual Learning and Recognition (Spring 2024), 16-825: Learning for 3D Vision (Spring 2023).
  • Reviewer Service: ICCV 2021, AAAI 2022, WACV 2022, CVPR 2022, CVPR 2023 (+ Emergency reviewer), ICCV 2023, NeurIPS 2023, Pattern Recognition Journal, WACV 2024 (+ Emergency reviewer), CVPR 2024.
  • Volunteer at NeurIPS 2022 High School Outreach Program.
  • Mentor at CMU AI Undergraduate Mentoring Program (Fall 2022, Spring 2023, Fall 2023).
  • Mentor at Spring 2023 CMU Research Mixer for undergraduate students organized by DPAC Undergraduate Research Working Group.

Teaching Assistant for 16-824: Visual Learning and Recognition (Spring 2024)
Teaching Assistant for 16-825: Learning for 3D Vision (Spring 2023)

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