Search Results for author: Octavia Camps

Found 19 papers, 5 papers with code

Key Frame Proposal Network for Efficient Pose Estimation in Videos

1 code implementation ECCV 2020 Yuexi Zhang, Yin Wang, Octavia Camps, Mario Sznaier

Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames.

Pose Estimation

Learning Disentangled Representations of Video with Missing Data

1 code implementation23 Jun 2020 Armand Comas-Massagué, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu

Missing data poses significant challenges while learning representations of video sequences.

Towards Visually Explaining Variational Autoencoders

2 code implementations CVPR 2020 Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J. Radke, Octavia Camps

We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.

Disentanglement

DYAN: A Dynamical Atoms-Based Network for Video Prediction

no code implementations ECCV 2018 Wenqian Liu, Abhishek Sharma, Octavia Camps, Mario Sznaier

The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning.

Representation Learning Video Prediction

MoNet: Moments Embedding Network

no code implementations CVPR 2018 Mengran Gou, Fei Xiong, Octavia Camps, Mario Sznaier

In addition, we propose a novel sub-matrix square-root layer, which can be used to normalize the output of the convolution layer directly and mitigate the dimensionality problem with off-the-shelf compact pooling methods.

Multi-camera Multi-Object Tracking

no code implementations20 Sep 2017 Wenqian Liu, Octavia Camps, Mario Sznaier

For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking.

Multi-Object Tracking Visual Tracking

Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming

no code implementations CVPR 2016 Yongfang Cheng, Yin Wang, Mario Sznaier, Octavia Camps

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers.

Solving Temporal Puzzles

no code implementations CVPR 2016 Caglayan Dicle, Burak Yilmaz, Octavia Camps, Mario Sznaier

Many physical phenomena, within short time windows, can be explained by low order differential relations.

Time Series

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

3 code implementations31 May 2016 Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke

To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques.

Metric Learning Person Re-Identification

Person Re-identification in Appearance Impaired Scenarios

no code implementations1 Apr 2016 Mengran Gou, Xikang Zhang, Angels Rates-Borras, Sadjad Asghari-Esfeden, Mario Sznaier, Octavia Camps

Our experiments on the original and the appearance impaired datasets demonstrate the benefits of incorporating dynamics-based information with appearance-based information to re-identification algorithms.

Person Re-Identification

A Convex Optimization Approach to Robust Fundamental Matrix Estimation

no code implementations CVPR 2015 Yongfang Cheng, Jose A. Lopez, Octavia Camps, Mario Sznaier

This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers.

Self Scaled Regularized Robust Regression

no code implementations CVPR 2015 Yin Wang, Caglayan Dicle, Mario Sznaier, Octavia Camps

Linear Robust Regression (LRR) seeks to find the parameters of a linear mapping from noisy data corrupted from outliers, such that the number of inliers (i. e. pairs of points where the fitting error of the model is less than a given bound) is maximized.

Robust Anomaly Detection Using Semidefinite Programming

no code implementations3 Apr 2015 Jose A. Lopez, Octavia Camps, Mario Sznaier

This paper presents a new approach, based on polynomial optimization and the method of moments, to the problem of anomaly detection.

Anomaly Detection

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