3.5k citations · 9.4k across the 54 of their papers we have counts for
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SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
Viraj Prabhu, Shivam Khare, Deeksha Kartik +1
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label…
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…
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko +1
Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-eff…
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
Samyak Datta, Oleksandr Maksymets, Judy Hoffman +3
Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with i…
TIDE: A General Toolbox for Identifying Object Detection Errors
Daniel Bolya, Sean Foley, James Hays +1
We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is app…