31 citations · 70 across the 19 of their papers we have counts for
22 papers
FloVMos: Optical Flow-based Medical Video Mosaicking
Jinyang Liu, Sandesh Ghimire, Chaman Singh +5
Biomedical imaging modalities often require a trade-off among resolution, field of view (FOV), and acquisition speed. Video mosaicking offers a strategy to overcome this limitation…
Generalization Error Analysis for Selective State-Space Models Through the Lens of Attention
Arya Honarpisheh, Mustafa Bozdag, Octavia Camps +1
State-space models (SSMs) have recently emerged as a compelling alternative to Transformers for sequence modeling tasks. This paper presents a theoretical generalization analysis o…
Face Reconstruction Transfer Attack as Out-of-Distribution Generalization
Yoon Gyo Jung, Jaewoo Park, Xingbo Dong +3
Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penet…
3D-HGS: 3D Half-Gaussian Splatting
Haolin Li, Jinyang Liu, Mario Sznaier +1
Photo-realistic image rendering from 3D scene reconstruction has advanced significantly with neural rendering techniques. Among these, 3D Gaussian Splatting (3D-GS) outperforms Neu…
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Jinyang Liu, Wondmgezahu Teshome, Sandesh Ghimire +2
Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video seq…
Inferring Relational Potentials in Interacting Systems
Armand Comas-Massagué, Yilun Du, Christian Fernandez +4
Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact r…
Enhancing Transformer Backbone for Egocentric Video Action Segmentation
Sakib Reza, Balaji Sundareshan, Mohsen Moghaddam +1
Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robot…
Cross-view Action Recognition via Contrastive View-invariant Representation
Yuexi Zhang, Dan Luo, Balaji Sundareshan +2
Cross view action recognition (CVAR) seeks to recognize a human action when observed from a previously unseen viewpoint. This is a challenging problem since the appearance of an ac…
Geometry of Score Based Generative Models
Sandesh Ghimire, Jinyang Liu, Armand Comas +4
In this work, we look at Score-based generative models (also called diffusion generative models) from a geometric perspective. From a new view point, we prove that both the forward…
Divide and Compose with Score Based Generative Models
Sandesh Ghimire, Armand Comas, Davin Hill +3
While score based generative models, or diffusion models, have found success in image synthesis, they are often coupled with text data or image label to be able to manipulate and c…
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Tooba Imtiaz, Morgan Kohler, Jared Miller +5
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can d…
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
Max Torop, Sandesh Ghimire, Wenqian Liu +5
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OO…
Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective
Armand Comas, Sandesh Ghimire, Haolin Li +2
Human interpretation of the world encompasses the use of symbols to categorize sensory inputs and compose them in a hierarchical manner. One of the long-term objectives of Computer…
Key Frame Proposal Network for Efficient Pose Estimation in Videos
Yuexi Zhang, Yin Wang, Octavia Camps +1
Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames. In this paper, we propose a novel method c…
Learning Disentangled Representations of Video with Missing Data
Armand Comas-Massagué, Chi Zhang, Zlatan Feric +2
Missing data poses significant challenges while learning representations of video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that…
Towards Visually Explaining Variational Autoencoders
Wenqian Liu, Runze Li, Meng Zheng +5
Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, grad…
MoNet: Moments Embedding Network
Mengran Gou, Fei Xiong, Octavia Camps +1
Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple visi…
DYAN: A Dynamical Atoms-Based Network for Video Prediction
Wenqian Liu, Abhishek Sharma, Octavia Camps +1
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 unsuper…
Multi-camera Multi-Object Tracking
Wenqian Liu, Octavia Camps, Mario Sznaier
In this paper, we propose a pipeline for multi-target visual tracking under multi-camera system. For multi-camera system tracking problem, efficient data association across cameras…
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Srikrishna Karanam, Mengran Gou, Ziyan Wu +3
Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision…
Person Re-identification in Appearance Impaired Scenarios
Mengran Gou, Xikang Zhang, Angels Rates-Borras +3
Person re-identification is critical in surveillance applications. Current approaches rely on appearance based features extracted from a single or multiple shots of the target and…
Robust Anomaly Detection Using Semidefinite Programming
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. The proposed technique only requires informatio…