no code implementations • 23 Oct 2023 • Armand Comas-Massagué, Yilun Du, Christian Fernandez, Sandesh Ghimire, Mario Sznaier, Joshua B. Tenenbaum, Octavia Camps
In this work, we propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions that enables greater flexibility in trajectory modeling: it discovers a set of relational potentials, represented as energy functions, which when minimized reconstruct the original trajectory.
no code implementations • 19 May 2023 • Sakib Reza, Balaji Sundareshan, Mohsen Moghaddam, Octavia Camps
Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models.
no code implementations • 2 May 2023 • Yuexi Zhang, Dan Luo, Balaji Sundareshan, Octavia Camps, Mario Sznaier
Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint.
no code implementations • 9 Feb 2023 • Sandesh Ghimire, Jinyang Liu, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy
We demonstrate that looking from geometric perspective enables us to answer many of these questions and provide new interpretations to some known results.
no code implementations • 5 Feb 2023 • Sandesh Ghimire, Armand Comas, Davin Hill, Aria Masoomi, Octavia Camps, Jennifer Dy
Towards the direction of having more control over image manipulation and conditional generation, we propose to learn image components in an unsupervised manner so that we can compose those components to generate and manipulate images in informed manner.
no code implementations • 14 Dec 2022 • Tooba Imtiaz, Morgan Kohler, Jared Miller, Zifeng Wang, Mario Sznaier, Octavia Camps, Jennifer Dy
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
no code implementations • 8 Nov 2021 • Max Torop, Sandesh Ghimire, Wenqian Liu, Dana H. Brooks, Octavia Camps, Milind Rajadhyaksha, Jennifer Dy, Kivanc Kose
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data.
no code implementations • 1 Oct 2021 • Armand Comas, Sandesh Ghimire, Haolin Li, Mario Sznaier, Octavia Camps
Human interpretation of the world encompasses the use of symbols to categorize sensory inputs and compose them in a hierarchical manner.
1 code implementation • NeurIPS 2020 • Armand Comas, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu
Missing data poses significant challenges while learning representations of video sequences.
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.
1 code implementation • 23 Jun 2020 • Armand Comas-Massagué, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu
Missing data poses significant challenges while learning representations of video sequences.
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.
no code implementations • CVPR 2018 • Mario Sznaier, Octavia Camps
This paper addresses the problem of subspace clustering in the presence of outliers.
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.
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.
no code implementations • ICCV 2017 • Xikang Zhang, Bengisu Ozbay, Mario Sznaier, Octavia Camps
This paper considers the multi-camera motion segmentation problem using unsynchronized videos.
no code implementations • 20 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.
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.
no code implementations • CVPR 2016 • Xikang Zhang, Yin Wang, Mengran Gou, Mario Sznaier, Octavia Camps
In this paper we propose a new framework to compare and classify temporal sequences.
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.
3 code implementations • 31 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.
no code implementations • 1 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.
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.
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.
no code implementations • 3 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.