Search Results for author: Weidi Xie

Found 30 papers, 15 papers with code

Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning

1 code implementation26 May 2021 Pak-Hei Yeung, Ana I. L. Namburete, Weidi Xie

The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i. e. semi-automatic 3D segmentation).

Self-Supervised Learning

Self-supervised Video Object Segmentation by Motion Grouping

no code implementations15 Apr 2021 Charig Yang, Hala Lamdouar, Erika Lu, Andrew Zisserman, Weidi Xie

We additionally evaluate on a challenging camouflage dataset (MoCA), significantly outperforming the other self-supervised approaches, and comparing favourably to the top supervised approach, highlighting the importance of motion cues, and the potential bias towards visual appearance in existing video segmentation models.

Motion Segmentation Optical Flow Estimation +4

All you need are a few pixels: semantic segmentation with PixelPick

no code implementations13 Apr 2021 Gyungin Shin, Weidi Xie, Samuel Albanie

A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training.

Active Learning Semantic Segmentation

Quantum Self-Supervised Learning

1 code implementation26 Mar 2021 Ben Jaderberg, Lewis W. Anderson, Weidi Xie, Samuel Albanie, Martin Kiffner, Dieter Jaksch

The popularisation of neural networks has seen incredible advances in pattern recognition, driven by the supervised learning of human annotations.

Self-Supervised Learning

NeRF--: Neural Radiance Fields Without Known Camera Parameters

3 code implementations14 Feb 2021 ZiRui Wang, Shangzhe Wu, Weidi Xie, Min Chen, Victor Adrian Prisacariu

This paper tackles the problem of novel view synthesis (NVS) from 2D images without known camera poses and intrinsics.

Novel View Synthesis Structure from Motion

Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation

no code implementations23 Nov 2020 Hala Lamdouar, Charig Yang, Weidi Xie, Andrew Zisserman

We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for breaking camouflage, namely, a differentiable registration module to align consecutive frames based on the background, which effectively emphasises the object boundary in the difference image, and a motion segmentation module with memory that discovers the moving objects, while maintaining the object permanence even when motion is absent at some point.

Motion Segmentation Object Discovery +1

Layered Neural Rendering for Retiming People in Video

1 code implementation16 Sep 2020 Erika Lu, Forrester Cole, Tali Dekel, Weidi Xie, Andrew Zisserman, David Salesin, William T. Freeman, Michael Rubinstein

We present a method for retiming people in an ordinary, natural video---manipulating and editing the time in which different motions of individuals in the video occur.

Neural Rendering

Inducing Predictive Uncertainty Estimation for Face Recognition

no code implementations1 Sep 2020 Weidi Xie, Jeffrey Byrne, Andrew Zisserman

We describe three use cases on the public IJB-C face verification benchmark: (i) to improve 1:1 image-based verification error rates by rejecting low-quality face images; (ii) to improve quality score based fusion performance on the 1:1 set-based verification benchmark; and (iii) its use as a quality measure for selecting high quality (unblurred, good lighting, more frontal) faces from a collection, e. g. for automatic enrolment or display.

Face Recognition Face Verification

Memory-augmented Dense Predictive Coding for Video Representation Learning

1 code implementation ECCV 2020 Tengda Han, Weidi Xie, Andrew Zisserman

The objective of this paper is self-supervised learning from video, in particular for representations for action recognition.

Action Classification Action Recognition +4

Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval

2 code implementations ECCV 2020 Andrew Brown, Weidi Xie, Vicky Kalogeiton, Andrew Zisserman

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods.

Image Instance Retrieval Metric Learning +1

Self-supervised Video Object Segmentation

no code implementations22 Jun 2020 Fangrui Zhu, Li Zhang, Yanwei Fu, Guodong Guo, Weidi Xie

The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a. k. a.

One-shot visual object segmentation Representation Learning +2

VGGSound: A Large-scale Audio-Visual Dataset

1 code implementation29 Apr 2020 Honglie Chen, Weidi Xie, Andrea Vedaldi, Andrew Zisserman

Our goal is to collect a large-scale audio-visual dataset with low label noise from videos in the wild using computer vision techniques.

Image Classification

MAST: A Memory-Augmented Self-supervised Tracker

2 code implementations CVPR 2020 Zihang Lai, Erika Lu, Weidi Xie

Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge

no code implementations5 Dec 2019 Joon Son Chung, Arsha Nagrani, Ernesto Coto, Weidi Xie, Mitchell McLaren, Douglas A. Reynolds, Andrew Zisserman

The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data.

Speaker Recognition

Video Representation Learning by Dense Predictive Coding

1 code implementation10 Sep 2019 Tengda Han, Weidi Xie, Andrew Zisserman

The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition.

Representation Learning Self-Supervised Action Recognition +1

AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations

no code implementations14 Aug 2019 Honglie Chen, Weidi Xie, Andrea Vedaldi, Andrew Zisserman

We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise.

Self-supervised Learning for Video Correspondence Flow

1 code implementation2 May 2019 Zihang Lai, Weidi Xie

Fourth, in order to shed light on the potential of self-supervised learning on the task of video correspondence flow, we probe the upper bound by training on additional data, \ie more diverse videos, further demonstrating significant improvements on video segmentation.

Self-Supervised Learning Video Correspondence Flow +2

Utterance-level Aggregation For Speaker Recognition In The Wild

6 code implementations26 Feb 2019 Weidi Xie, Arsha Nagrani, Joon Son Chung, Andrew Zisserman

The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals.

Speaker Recognition Text-Independent Speaker Verification

Class-Agnostic Counting

1 code implementation1 Nov 2018 Erika Lu, Weidi Xie, Andrew Zisserman

The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images.

Crowd Counting Few-Shot Learning +1

Comparator Networks

no code implementations ECCV 2018 Weidi Xie, Li Shen, Andrew Zisserman

Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair--this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage high-quality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models.

Face Recognition Image Classification +1

Multicolumn Networks for Face Recognition

1 code implementation24 Jul 2018 Weidi Xie, Andrew Zisserman

In this paper, we design a neural network architecture that learns to aggregate based on both "visual" quality (resolution, illumination), and "content" quality (relative importance for discriminative classification).

Ranked #5 on Face Verification on IJB-C (TAR @ FAR=0.01 metric)

Face Recognition General Classification

Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks

no code implementations3 Nov 2017 Davis M. Vigneault, Weidi Xie, Carolyn Y. Ho, David A. Bluemke, J. Alison Noble

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses.

Semantic Segmentation

VGGFace2: A dataset for recognising faces across pose and age

16 code implementations23 Oct 2017 Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, Andrew Zisserman

The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.

Ranked #3 on Face Verification on IJB-C (TAR @ FAR=0.01 metric)

Face Recognition Face Verification +1

Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks

no code implementations17 Jul 2017 Yipeng Hu, Eli Gibson, Li-Lin Lee, Weidi Xie, Dean C. Barratt, Tom Vercauteren, J. Alison Noble

Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration.

Image Registration Medical Procedure

Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

no code implementations12 Apr 2017 Davis M. Vigneault, Weidi Xie, David A. Bluemke, J. Alison Noble

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences.

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