Search Results for author: Heechul Jung

Found 16 papers, 6 papers with code

Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning

1 code implementation26 Jan 2023 Athul Shibu, Abhishek Kumar, Heechul Jung, Dong-Gyu Lee

Convolutional Neural Networks (CNNs) have a large number of parameters and take significantly large hardware resources to compute, so edge devices struggle to run high-level networks.


Fair Comparison between Efficient Attentions

1 code implementation1 Jun 2022 Jiuk Hong, Chaehyeon Lee, Soyoun Bang, Heechul Jung

Transformers have been successfully used in various fields and are becoming the standard tools in computer vision.

Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints

no code implementations19 Nov 2021 Jaesin Ahn, Jiuk Hong, Jeongwoo Ju, Heechul Jung

The proposed method achieved $71. 4\%$ with a few parameters (of $3. 1M$) on the ImageNet-1k dataset compared to that required by the original transformer model of XCiT-N12 ($69. 9\%$).

Image Classification Inductive Bias +1

Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

1 code implementation31 May 2021 Chaehyeon Lee, Junghoon Seo, Heechul Jung

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes.

object-detection Object Detection

Extending Contrastive Learning to Unsupervised Coreset Selection

1 code implementation5 Mar 2021 Jeongwoo Ju, Heechul Jung, Yoonju Oh, Junmo Kim

Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data.

Contrastive Learning

FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

1 code implementation CVPR 2021 Jaemin Na, Heechul Jung, Hyung Jin Chang, Wonjun Hwang

However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies.

Unsupervised Domain Adaptation

Development of Fast Refinement Detectors on AI Edge Platforms

1 code implementation24 Sep 2019 Min-Kook Choi, Heechul Jung

With the improvements in the object detection networks, several variations of object detection networks have been achieved impressive performance.

object-detection Object Detection

Co-occurrence matrix analysis-based semi-supervised training for object detection

no code implementations20 Feb 2018 Min-Kook Choi, Jaehyeong Park, Jihun Jung, Heechul Jung, Jin-Hee Lee, Woong Jae Won, Woo Young Jung, Jincheol Kim, Soon Kwon

We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training.

object-detection Object Detection +2

Less-forgetful Learning for Domain Expansion in Deep Neural Networks

no code implementations16 Nov 2017 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain.

Domain Adaptation Image Classification

Can Deep Neural Networks Match the Related Objects?: A Survey on ImageNet-trained Classification Models

no code implementations12 Sep 2017 Han S. Lee, Heechul Jung, Alex A. Agarwal, Junmo Kim

To verify how DNNs understand the relatedness between object classes, we conducted experiments on the image database provided in cognitive psychology.

Association General Classification +1

End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation

no code implementations20 Dec 2016 Heechul Jung, Min-Kook Choi, Kwon Soon, Woo Young Jung

Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e. g., when all pedestrians are walking on the sidewalk).

Semantic Segmentation

Less-forgetting Learning in Deep Neural Networks

no code implementations1 Jul 2016 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments.

Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition

no code implementations ICCV 2015 Heechul Jung, Sihaeng Lee, Junho Yim, Sunjeong Park, Junmo Kim

Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.

Facial Expression Recognition (FER)

Rotating Your Face Using Multi-Task Deep Neural Network

no code implementations CVPR 2015 Junho Yim, Heechul Jung, ByungIn Yoo, Changkyu Choi, Dusik Park, Junmo Kim

This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity.

Face Recognition

Deep Temporal Appearance-Geometry Network for Facial Expression Recognition

no code implementations5 Mar 2015 Heechul Jung, Sihaeng Lee, Sunjeong Park, Injae Lee, Chunghyun Ahn, Junmo Kim

Furthermore, one of the main contributions of this paper is that our deep network catches the facial action points automatically.

Facial Expression Recognition (FER)

Rigid Motion Segmentation using Randomized Voting

no code implementations CVPR 2014 Heechul Jung, Jeongwoo Ju, Junmo Kim

For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted; it consists of two and three rigid motions.

Motion Segmentation

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