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1. Self-supervised method for scene-flow estimation of LiDAR point clouds. Method is trained and tested on the nuScenes and KITTI datasets in TensorFlow. (CVPR 2020)

   Python 68 5

2. Self-supervised algorithm for learning representations from ego-centric video data. Code is tested on EPIC-Kitchens-100 and Ego4D in PyTorch. (NeurIPS 2022)

   Python 13 1

3. Self-supervised method for completing partial LiDAR point clouds. Trained and tested on ShapeNet and SemanticKITTI in TensorFlow. (BMVC 2021)

   Python 14 3

4. Detecting depression levels in employees from videos of DAIC-WOZ dataset using LSTMs and Facial Action Units as input.

   Jupyter Notebook 31 6

5. Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

   Python 12 3