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Found 18 papers, 10 papers with code
Tracking through Containers and Occluders in the Wild
Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems.
What You Can Reconstruct From a Shadow
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states.
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications
Egocentric videos offer fine-grained information for high-fidelity modeling of human behaviors.
Shadows Shed Light on 3D Objects
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Revealing Occlusions with 4D Neural Fields
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence.
The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video
The ability to reliably estimate physiological signals from video is a powerful tool in low-cost, pre-clinical health monitoring.
MAAD: A Model and Dataset for "Attended Awareness" in Driving
Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness.
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets.
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting.
Ranked #4 on
Gaze Estimation
on Gaze360
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.
Street-view change detection with deconvolutional networks
We propose a system for performing structural change detection in street-view videos captured by a vehicle-mounted monocular camera over time.
A Dataset To Evaluate The Representations Learned By Video Prediction Models
We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks.
gvnn: Neural Network Library for Geometric Computer Vision
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs).
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
We are interested in automatic scene understanding from geometric cues.
