In our model, medical text annotation is introduced to compensate for the quality deficiency in image data.
Ranked #1 on Medical Image Segmentation on MoNuSeg
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence.
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object.
Ranked #1 on Multi-Object Tracking on MOT20 (using extra training data)
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks.
We test language models on our forecasting task and find that performance is far below a human expert baseline.
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.
To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction.
Ranked #13 on Motion Forecasting on Argoverse CVPR 2020