65 citations · 233 across the 23 of their papers we have counts for
5 papers · 1 filter
Learning Compositional Representations for Few-Shot Recognition
Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from j…
A Structured Model For Action Detection
Yubo Zhang, Pavel Tokmakov, Martial Hebert +1
A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large data…
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
Kashyap Chitta, Jianwei Feng, Martial Hebert
Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks stil…
Iterative Transformer Network for 3D Point Cloud
Wentao Yuan, David Held, Christoph Mertz +1
3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on 3D understanding…
PCN: Point Completion Network
Wentao Yuan, Tejas Khot, David Held +2
Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we…