Search Results for author:
Found 19 papers, 11 papers with code
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
A Hierarchical Assessment of Adversarial Severity
Adversarial Robustness is a growing field that evidences the brittleness of neural networks.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
APES: Audiovisual Person Search in Untrimmed Video
To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval.
Panoptic Narrative Grounding
This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem.
ISINet: An Instance-Based Approach for Surgical Instrument Segmentation
We study the task of semantic segmentation of surgical instruments in robotic-assisted surgery scenes.
Active Speakers in Context
Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker.
MAIN: Multi-Attention Instance Network for Video Segmentation
Instance-level video segmentation requires a solid integration of spatial and temporal information.
BAOD: Budget-Aware Object Detection
We adopt a hybrid supervised learning framework to train the object detector from both these types of annotation.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Multi-View Dynamic Facial Action Unit Detection
We then move to the novel setup of the FERA 2017 Challenge, in which we propose a multi-view extension of our approach that operates by first predicting the viewpoint from which the video was taken, and then evaluating an ensemble of action unit detectors that were trained for that specific viewpoint.
Ranked #3 on
Facial Action Unit Detection
on BP4D
Aligning 3D Models to RGB-D Images of Cluttered Scenes
The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library.
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG).
Learning to Segment Moving Objects in Videos
We segment moving objects in videos by ranking spatio-temporal segment proposals according to "moving objectness": how likely they are to contain a moving object.
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction
Furthermore, NRSfM needs to be robust to noise in both segmentation and tracking, e. g., drifting, segmentation ``leaking'', optical flow ``bleeding'' etc.
Multiscale Combinatorial Grouping
We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG).
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
We address the problems of contour detection, bottomup grouping and semantic segmentation using RGB-D data.
Articulated Pose Estimation Using Discriminative Armlet Classifiers
We propose a novel approach for human pose estimation in real-world cluttered scenes, and focus on the challenging problem of predicting the pose of both arms for each person in the image.
