630 citations · 2.4k across the 27 of their papers we have counts for
19 papers · 1 filter
Learning from Lexical Perturbations for Consistent Visual Question Answering
Spencer Whitehead, Hui Wu, Yi Ren Fung +3
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modul…
How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Kate Saenko +1
We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance…
Regularizing Action Policies for Smooth Control with Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Renato Mancuso +1
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL polici…
Fine-grained Angular Contrastive Learning with Coarse Labels
Guy Bukchin, Eli Schwartz, Kate Saenko +4
Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This…
Temporal Action Detection with Multi-level Supervision
Baifeng Shi, Qi Dai, Judy Hoffman +3
Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to…
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi, Judy Hoffman, Kate Saenko +2
Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to util…