53 citations · 126 across the 14 of their papers we have counts for
16 papers
Long-Distance Gesture Recognition using Dynamic Neural Networks
Shubhang Bhatnagar, Sharath Gopal, Narendra Ahuja +1
Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where hu…
Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation
Moitreya Chatterjee, Narendra Ahuja, Anoop Cherian
There exists an unequivocal distinction between the sound produced by a static source and that produced by a moving one, especially when the source moves towards or away from the m…
Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras
Aniket Shirke, Aziz Saifuddin, Achleshwar Luthra +10
Increasing demand for meat products combined with farm labor shortages has resulted in a need to develop new real-time solutions to monitor animals effectively. Significant progres…
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction
Moitreya Chatterjee, Narendra Ahuja, Anoop Cherian
Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate…
Visual Scene Graphs for Audio Source Separation
Moitreya Chatterjee, Jonathan Le Roux, Narendra Ahuja +1
State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches ofte…
Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition
Xiaodan Hu, Narendra Ahuja
Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dan…
Sound2Sight: Generating Visual Dynamics from Sound and Context
Anoop Cherian, Moitreya Chatterjee, Narendra Ahuja
Learning associations across modalities is critical for robust multimodal reasoning, especially when a modality may be missing during inference. In this paper, we study this proble…
Coreset-Based Neural Network Compression
Abhimanyu Dubey, Moitreya Chatterjee, Narendra Ahuja
We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weig…
DeepMVS: Learning Multi-view Stereopsis
Po-Han Huang, Kevin Matzen, Johannes Kopf +2
We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction. Taking an arbitrary number of posed images as input, we first produce a set…
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Shun Zhang, Jia-Bin Huang, Jongwoo Lim +4
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in sc…
Joint Image Filtering with Deep Convolutional Networks
Yijun Li, Jia-Bin Huang, Narendra Ahuja +1
Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatia…
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja +1
Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution. However, existing methods often require a large number of ne…
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja +1
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolu…
Superpixel Hierarchy
Xing Wei, Qingxiong Yang, Yihong Gong +2
Superpixel segmentation is becoming ubiquitous in computer vision. In practice, an object can either be represented by a number of segments in finer levels of detail or included in…
A Probabilistic Framework for Discriminative Dictionary Learning
Bernard Ghanem, Narendra Ahuja
In this paper, we address the problem of discriminative dictionary learning (DDL), where sparse linear representation and classification are combined in a probabilistic framework.…
MIS-Boost: Multiple Instance Selection Boosting
Emre Akbas, Bernard Ghanem, Narendra Ahuja
In this paper, we present a new multiple instance learning (MIL) method, called MIS-Boost, which learns discriminative instance prototypes by explicit instance selection in a boost…