105 citations · 394 across the 23 of their papers we have counts for
7 papers · 1 filter
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
Subarna Tripathi, Zachary C. Lipton, Serge Belongie +1
Given the vast amounts of video available online, and recent breakthroughs in object detection with static images, object detection in video offers a promising new frontier. Howeve…
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
Igor Fedorov, Ritwik Giri, Bhaskar D. Rao +1
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the abil…
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
Igor Fedorov, Alican Nalci, Ritwik Giri +3
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered…
Hand Segmentation for Hand-Object Interaction from Depth map
Byeongkeun Kang, Kar-Han Tan, Nan Jiang +3
Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. Ho…
Joint Defogging and Demosaicking
Y. J. Lee, K. Hirakawa, T. Q. Nguyen
Image defogging is a technique used extensively for enhancing visual quality of images in bad weather condition. Even though defogging algorithms have been well studied, defogging…
Detecting Temporally Consistent Objects in Videos through Object Class Label Propagation
Subarna Tripathi, Serge Belongie, Youngbae Hwang +1
Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object…
Adaptive Image Denoising by Mixture Adaptation
Enming Luo, Stanley H. Chan, Truong Q. Nguyen
We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a gen…