1 citations · 2 across the 5 of their papers we have counts for
8 papers
Skill Generalization with Verbs
Rachel Ma, Lyndon Lam, Benjamin A. Spiegel +6
It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a give…
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Aditya Ganeshan, Alexis Vallet, Yasunori Kudo +5
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets. Labelling is a tedious process that can take hours per image. Automatica…
Meta-learning Extractors for Music Source Separation
David Samuel, Aditya Ganeshan, Jason Naradowsky
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor m…
FDA: Feature Disruptive Attack
Aditya Ganeshan, B. S. Vivek, R. Venkatesh Babu
Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image sample…
Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence
Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan +1
Understanding the geometry and pose of objects in 2D images is a fundamental necessity for a wide range of real world applications. Driven by deep neural networks, recent methods h…
iSPA-Net: Iterative Semantic Pose Alignment Network
Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V. +2
Understanding and extracting 3D information of objects from monocular 2D images is a fundamental problem in computer vision. In the task of 3D object pose estimation, recent data d…