Search Results for author: Kwang In Kim

Found 30 papers, 5 papers with code

Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution

no code implementations27 Apr 2022 Tze Ho Elden Tse, Kwang In Kim, Ales Leonardis, Hyung Jin Chang

Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality.

3D Pose Estimation Object Reconstruction

Testing using Privileged Information by Adapting Features with Statistical Dependence

no code implementations ICCV 2021 Kwang In Kim, James Tompkin

Then, we empirically estimate and strengthen the statistical dependence between the initial noisy predictor and the additional features via manifold denoising.

Denoising

DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation

no code implementations15 Jul 2021 Jake Deane, Sinead Kearney, Kwang In Kim, Darren Cosker

Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks.

3D Pose Estimation Image Generation

GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes

no code implementations24 Jun 2021 Youssef A. Mejjati, Isa Milefchik, Aaron Gokaslan, Oliver Wang, Kwang In Kim, James Tompkin

We present an algorithm that learns a coarse 3D representation of objects from unposed multi-view 2D mask supervision, then uses it to generate detailed mask and image texture.

Image Generation Object Reconstruction

End-to-End Detection and Pose Estimation of Two Interacting Hands

no code implementations ICCV 2021 Dong Uk Kim, Kwang In Kim, Seungryul Baek

Three dimensional hand pose estimation has reached a level of maturity, enabling real-world applications for single-hand cases.

Hand Pose Estimation

Look here! A parametric learning based approach to redirect visual attention

no code implementations ECCV 2020 Youssef Alami Mejjati, Celso F. Gomez, Kwang In Kim, Eli Shechtman, Zoya Bylinskii

Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region.

Combining Task Predictors via Enhancing Joint Predictability

no code implementations ECCV 2020 Kwang In Kim, Christian Richardt, Hyung Jin Chang

Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors.

Multi-class Classification

RGBD-Dog: Predicting Canine Pose from RGBD Sensors

1 code implementation CVPR 2020 Sinead Kearney, Wenbin Li, Martin Parsons, Kwang In Kim, Darren Cosker

We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images.

Pose Estimation Pose Prediction

Task-Aware Variational Adversarial Active Learning

no code implementations CVPR 2021 Kwanyoung Kim, Dongwon Park, Kwang In Kim, Se Young Chun

Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.

Active Learning Semantic Segmentation

Generating Object Stamps

1 code implementation1 Jan 2020 Youssef Alami Mejjati, Zejiang Shen, Michael Snower, Aaron Gokaslan, Oliver Wang, James Tompkin, Kwang In Kim

We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture.

Emergence of Implicit Filter Sparsity in Convolutional Neural Networks

no code implementations ICML Workshop Deep_Phenomen 2019 Dushyant Mehta, Kwang In Kim, Christian Theobalt

We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay.

L2 Regularization

Implicit Filter Sparsification In Convolutional Neural Networks

no code implementations13 May 2019 Dushyant Mehta, Kwang In Kim, Christian Theobalt

We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay.

L2 Regularization

Joint Manifold Diffusion for Combining Predictions on Decoupled Observations

no code implementations CVPR 2019 Kwang In Kim, Hyung Jin Chang

We present a new predictor combination algorithm that improves a given task predictor based on potentially relevant reference predictors.

Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering

no code implementations CVPR 2019 Seungryul Baek, Kwang In Kim, Tae-Kyun Kim

Once the model is successfully fitted to input RGB images, its meshes i. e. shapes and articulations, are realistic, and we augment view-points on top of estimated dense hand poses.

3D Hand Pose Estimation 3D Pose Estimation +2

Unsupervised Attention-guided Image-to-Image Translation

1 code implementation NeurIPS 2018 Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.

Translation Unsupervised Image-To-Image Translation

On Implicit Filter Level Sparsity in Convolutional Neural Networks

no code implementations CVPR 2019 Dushyant Mehta, Kwang In Kim, Christian Theobalt

We investigate filter level sparsity that emerges in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay.

L2 Regularization

Improving Shape Deformation in Unsupervised Image-to-Image Translation

4 code implementations ECCV 2018 Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, James Tompkin

Unsupervised image-to-image translation techniques are able to map local texture between two domains, but they are typically unsuccessful when the domains require larger shape change.

Semantic Segmentation Translation +1

Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios

no code implementations11 Jun 2018 Juil Sock, Kwang In Kim, Caner Sahin, Tae-Kyun Kim

Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects.

3D Pose Estimation Multi-Task Learning

Unsupervised Attention-guided Image to Image Translation

2 code implementations6 Jun 2018 Youssef A. Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.

Translation Unsupervised Image-To-Image Translation

High-Order Tensor Regularization With Application to Attribute Ranking

no code implementations CVPR 2018 Kwang In Kim, Juhyun Park, James Tompkin

When learning functions on manifolds, we can improve performance by regularizing with respect to the intrinsic manifold geometry rather than the ambient space.

Augmented Skeleton Space Transfer for Depth-based Hand Pose Estimation

no code implementations CVPR 2018 Seungryul Baek, Kwang In Kim, Tae-Kyun Kim

By training the HPG and HPE in a single unified optimization framework enforcing that 1) the HPE agrees with the paired depth and skeleton entries; and 2) the HPG-HPE combination satisfies the cyclic consistency (both the input and the output of HPG-HPE are skeletons) observed via the newly generated unpaired skeletons, our algorithm constructs a HPE which is robust to variations that go beyond the coverage of the existing database.

Hand Pose Estimation

Ranking CGANs: Subjective Control over Semantic Image Attributes

no code implementations11 Apr 2018 Yassir Saquil, Kwang In Kim, Peter Hall

In this paper, we investigate the use of generative adversarial networks in the task of image generation according to subjective measures of semantic attributes.

Image Generation

Predictor Combination at Test Time

no code implementations ICCV 2017 Kwang In Kim, James Tompkin, Christian Richardt

We present an algorithm for test-time combination of a set of reference predictors with unknown parametric forms.

Denoising Transfer Learning

Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking

no code implementations12 Jun 2017 James Tompkin, Kwang In Kim, Hanspeter Pfister, Christian Theobalt

Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect.

Deep Convolutional Decision Jungle for Image Classification

no code implementations6 Jun 2017 Seungryul Baek, Kwang In Kim, Tae-Kyun Kim

Each response map-or node-in both the convolutional and fully-connected layers selectively respond to class labels s. t.

Classification Face Verification +2

Real-time Online Action Detection Forests using Spatio-temporal Contexts

no code implementations28 Oct 2016 Seungryul Baek, Kwang In Kim, Tae-Kyun Kim

Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements and 2) the localization and classification of actions have to be performed even before they are fully observed.

Action Detection Frame

Context-guided diffusion for label propagation on graphs

no code implementations ICCV 2015 Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt

Existing approaches for diffusion on graphs, e. g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer.

Local High-order Regularization on Data Manifolds

no code implementations CVPR 2015 Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt

The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems.

Dimensionality Reduction

Semi-supervised Learning with Explicit Relationship Regularization

no code implementations CVPR 2015 Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt

In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points.

Dimensionality Reduction General Classification

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