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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Mask Transfiner for High-Quality Instance Segmentation

Mask Transfiner for High-Quality Instance Segmentation

arXiv 2021 we present Mask Transfiner for high-quality and efficient instance segmentation, which predicts highly accurate instance masks at a low computational cost using quadtree transformer.

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

CVPR 2020 Oral The largest driving video dataset for heterogeneous multitask learning.

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.