Visual Perception

Playing for Benchmarks

Project Overview

We are working on models and algorithms for visual perception. Our work has produced widely used convolutional network architectures and the dense random field model. We have also created large-scale datasets and benchmarks for training and evaluating broad-competence visual perception systems.


Tracking Objects as Points

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

Exploring Self-attention for Image Recognition

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer

Does Computer Vision Matter for Action?

Playing for Benchmarks

Dilated Residual Networks

Playing for Data: Ground Truth from Computer Games

Feature Space Optimization for Semantic Video Segmentation

Dense Monocular Depth Estimation in Complex Dynamic Scenes

Multi-Scale Context Aggregation by Dilated Convolutions

Parameter Learning and Convergent Inference for Dense Random Fields

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials