Recent Publications

Deep Layer Aggregation

Deep Layer Aggregation

CVPR 2018 Oral We augment standard architectures with deeper aggregation to better fuse information across layers.

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Frustratingly Simple Few-Shot Object Detection

Frustratingly Simple Few-Shot Object Detection

ICML 2020 State-of-the-art few-shot detection method with backpropagation learning.

Learning Saliency Propagation for Semi-Supervised Instance Segmentation

Learning Saliency Propagation for Semi-Supervised Instance Segmentation

CVPR 2020 We propose a ShapeProp module to propagate information between object detection and segmentation supervisions for Semi-Supervised Instance Segmentation.

Joint Monocular 3D Vehicle Detection and Tracking

Joint Monocular 3D Vehicle Detection and Tracking

ICCV 2019 We propose a novel online framework for 3D vehicle detection and tracking from monocular videos.

Disentangling Propagation and Generation for Video Prediction

Disentangling Propagation and Generation for Video Prediction

ICCV 2019 We describe a computational model for high-fidelity video prediction which disentangles motion-specific propagation from motion-agnostic generation.

Few Shot Object Detection via Feature Reweighting

Few Shot Object Detection via Feature Reweighting

ICCV 2019 We develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples.

Deep Mixture of Experts via Shallow Embedding

Deep Mixture of Experts via Shallow Embedding

UAI 2019 We explore a mixture of experts (MoE) approach to deep dynamic routing, which activates certain experts in the network on a per-example basis.

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

CVPR 2019 We propose Task-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the image representation to a new task in a meta learning fashion.

Hierarchical Discrete Distribution Decomposition for Match Density Estimation

Hierarchical Discrete Distribution Decomposition for Match Density Estimation

CVPR 2019 We propose Hierarchical Discrete Distribution Decomposition (HD^3), a framework suitable for learning probabilistic pixel correspondences in both optical flow and stereo matching.

Semantic Predictive Control for Explainable and Efficient Policy Learning

Semantic Predictive Control for Explainable and Efficient Policy Learning

ICRA 2019 We propose a driving policy learning framework that predicts feature representations of future visual inputs.