105 citations · 394 across the 23 of their papers we have counts for
28 papers
SM3D: Simultaneous Monocular Mapping and 3D Detection
Runfa Li, Truong Nguyen
Mapping and 3D detection are two major issues in vision-based robotics, and self-driving. While previous works only focus on each task separately, we present an innovative and effi…
In Defense of Scene Graphs for Image Captioning
Kien Nguyen, Subarna Tripathi, Bang Du +2
The mainstream image captioning models rely on Convolutional Neural Network (CNN) image features to generate captions via recurrent models. Recently, image scene graphs have been u…
Toward Joint Image Generation and Compression using Generative Adversarial Networks
Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen
In this paper, we present a generative adversarial network framework that generates compressed images instead of synthesizing raw RGB images and compressing them separately. In the…
Random Forest with Learned Representations for Semantic Segmentation
Byeongkeun Kang, Truong Q. Nguyen
In this work, we present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (…
Accurate and efficient video de-fencing using convolutional neural networks and temporal information
Chen Du, Byeongkeun Kang, Zheng Xu +2
De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers a…
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
Honggang Chen, Xiaohai He, Linbo Qing +2
JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-ra…
Correction by Projection: Denoising Images with Generative Adversarial Networks
Subarna Tripathi, Zachary C. Lipton, Truong Q. Nguyen
Generative adversarial networks (GANs) transform low-dimensional latent vectors into visually plausible images. If the real dataset contains only clean images, then ostensibly, the…
Image denoising with generalized Gaussian mixture model patch priors
Charles-Alban Deledalle, Shibin Parameswaran, Truong Q. Nguyen
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (E…
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up
Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis +1
Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration…
Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
Y. Lee, K. Hirakawa, T. Nguyen
We propose a novel lossless and lossy compression scheme for color filter array~(CFA) sampled images based on the wavelet transform of them. Our analysis suggests that the wavelet…
Depth Adaptive Deep Neural Network for Semantic Segmentation
Byeongkeun Kang, Yeejin Lee, Truong Q. Nguyen
In this work, we present the depth-adaptive deep neural network using a depth map for semantic segmentation. Typical deep neural networks receive inputs at the predetermined locati…
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Subarna Tripathi, Gokce Dane, Byeongkeun Kang +2
Deep convolutional Neural Networks (CNN) are the state-of-the-art performers for object detection task. It is well known that object detection requires more computation and memory…
Relevance Subject Machine: A Novel Person Re-identification Framework
Igor Fedorov, Ritwik Giri, Bhaskar D. Rao +1
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem. RSM falls under the category of Bayesian sparse recovery…
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…
Efficient Hand Articulations Tracking using Adaptive Hand Model and Depth map
Byeongkeun Kang, Yeejin Lee, Truong Q. Nguyen
Real-time hand articulations tracking is important for many applications such as interacting with virtual / augmented reality devices or tablets. However, most of existing algorith…
Real-time Sign Language Fingerspelling Recognition using Convolutional Neural Networks from Depth map
Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen
Sign language recognition is important for natural and convenient communication between deaf community and hearing majority. We take the highly efficient initial step of automatic…
Semantic Video Segmentation : Exploring Inference Efficiency
Subarna Tripathi, Serge Belongie, Youngbae Hwang +1
We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames. The key idea is to combine best of two worlds:…
Beyond Semantic Image Segmentation : Exploring Efficient Inference in Video
Subarna Tripathi, Serge Belongie, Truong Nguyen
We explore the efficiency of the CRF inference module beyond image level semantic segmentation. The key idea is to combine the best of two worlds of semantic co-labeling and exploi…