Search Results for author: B. V. K. Vijaya Kumar

Found 17 papers, 7 papers with code

Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

1 code implementation ECCV 2020 Yang Zou, Xiaodong Yang, Zhiding Yu, B. V. K. Vijaya Kumar, Jan Kautz

To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively.

Person Re-Identification Unsupervised Domain Adaptation

Towards Occlusion-Aware Multifocal Displays

no code implementations2 May 2020 Jen-Hao Rick Chang, Anat Levin, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

Multifocal displays, one of the classic approaches to satisfy the accommodation cue, place virtual content at multiple focal planes, each at a di erent depth.

Deep Classification Network for Monocular Depth Estimation

no code implementations23 Oct 2019 Azeez Oluwafemi, Yang Zou, B. V. K. Vijaya Kumar

Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks.

Classification General Classification +2

Confidence Regularized Self-Training

2 code implementations ICCV 2019 Yang Zou, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, Jinsong Wang

Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.

Image Classification Semantic Segmentation +2

Attention Control with Metric Learning Alignment for Image Set-based Recognition

no code implementations5 Aug 2019 Xiaofeng Liu, Zhenhua Guo, Jane You, B. V. K. Vijaya Kumar

The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set.

Face Recognition Face Verification +1

Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training

1 code implementation18 Oct 2018 Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, Jinsong Wang

In this paper, we propose a novel UDA framework based on an iterative self-training procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.

Semantic Segmentation Synthetic-to-Real Translation +1

Simultaneous Edge Alignment and Learning

3 code implementations ECCV 2018 Zhiding Yu, Weiyang Liu, Yang Zou, Chen Feng, Srikumar Ramalingam, B. V. K. Vijaya Kumar, Jan Kautz

Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.

Edge Detection Representation Learning

Towards Multifocal Displays with Dense Focal Stacks

no code implementations27 May 2018 Jen-Hao Rick Chang, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

We present a virtual reality display that is capable of generating a dense collection of depth/focal planes.

One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models

1 code implementation ICCV 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks.

Compressive Sensing Image Inpainting +1

One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models

2 code implementations29 Mar 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.

Compressive Sensing Image Inpainting +1

Structured Hough Voting for Vision-based Highway Border Detection

no code implementations18 Nov 2014 Zhiding Yu, Wende Zhang, B. V. K. Vijaya Kumar, Dan Levi

We propose a vision-based highway border detection algorithm using structured Hough voting.

Zero-Aliasing Correlation Filters for Object Recognition

no code implementations10 Nov 2014 Joseph A. Fernandez, Vishnu Naresh Boddeti, Andres Rodriguez, B. V. K. Vijaya Kumar

However, existing CF designs do not account for the fact that the multiplication of two DFTs in the frequency domain corresponds to a circular correlation in the time/spatial domain.

Object Localization Object Recognition

Maximum Margin Vector Correlation Filter

no code implementations24 Apr 2014 Vishnu Naresh Boddeti, B. V. K. Vijaya Kumar

Correlation Filters (CFs) are a class of classifiers which are designed for accurate pattern localization.

object-detection Object Detection

Correlation Filters for Object Alignment

no code implementations CVPR 2013 Vishnu Naresh Boddeti, Takeo Kanade, B. V. K. Vijaya Kumar

A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors.

Computer Vision

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