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I am a 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 had worked on self-supervised representation learning methods for multimodal audio-visual videos and as a RA at CMU, I worked on self-supervised algorithms for 3D LiDAR point clouds. I served in the organizing committee of WiCV@CVPR 2024, DEI Social Event, and Challenges/Opportunities for ECRs in Fast Paced AI Social Event! |
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I am interested in self-supervised learning, multi-modal machine learning, video representation learning, generative models, point clouds and autonomous driving. |
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Himangi Mittal, Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee [CVPR 2024] Paper / Arxiv We leverage a large video-language model for anticipating action sequences that are plausible in the real-world. We develop the understanding of plausibility of an action sequence in a large video-language model by introducing two objective functions, a counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss. |
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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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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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Sahana Prabhu Muraleedhara Himangi Mittal, Rajesh Varagani, Sweccha Jha, Shivendra Singh [Pattern Analysis and Applications Journal] Paper 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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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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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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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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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. |
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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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