Search Results for author: Daijin Kim

Found 16 papers, 5 papers with code

Weight-based Mask for Domain Adaptation

no code implementations22 Apr 2023 Eunseop Lee, Inhan Kim, Daijin Kim

In addition, SEM obtains class-related feature representations using the classifier weight and focuses on the foreground features for domain adaptation.

Unsupervised Domain Adaptation

Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

1 code implementation16 Aug 2022 Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.

Contrastive Learning Object +2

Learning mixture of domain-specific experts via disentangled factors for autonomous driving

1 code implementation AAAI 2022 Inhan Kim, Joonyeong Lee, Daijin Kim

The domain-specific features are used to calculate the importance weight of the domain-specific experts, and the disentangled domain-general and dynamic-object features are applied in estimating the control values.

Autonomous Driving Representation Learning

DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection

no code implementations20 Apr 2022 Dongmin Cha, Daijin Kim

Deep neural advancements have recently brought remarkable image synthesis performance to the field of image inpainting.

Image Inpainting Image Reconstruction

A Style-aware Discriminator for Controllable Image Translation

1 code implementation CVPR 2022 Kunhee Kim, Sanghun Park, Eunyeong Jeon, Taehun Kim, Daijin Kim

Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results.

Image Manipulation Multimodal Unsupervised Image-To-Image Translation +1

FA-GAN: Feature-Aware GAN for Text to Image Synthesis

no code implementations2 Sep 2021 Eunyeong Jeon, Kunhee Kim, Daijin Kim

Secondly, we introduce a feature-aware loss to provide the generator more direct supervision by employing the feature representation from the self-supervised discriminator.

Generative Adversarial Network Image Generation

Localization Uncertainty-Based Attention for Object Detection

no code implementations25 Aug 2021 Sanghun Park, Kunhee Kim, Eunseop Lee, Daijin Kim

Object detection has been applied in a wide variety of real world scenarios, so detection algorithms must provide confidence in the results to ensure that appropriate decisions can be made based on their results.

Object object-detection +1

UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation

1 code implementation6 Jul 2021 Taehun Kim, Hyemin Lee, Daijin Kim

We construct a modified version of U-Net shape network with additional encoder and decoder and compute a saliency map in each bottom-up stream prediction module and propagate to the next prediction module.

Medical Image Segmentation Segmentation

SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation

no code implementations8 Jun 2021 Taehun Kim, Jinseong Kim, Daijin Kim

For this reason, we propose Spatial Context Memoization (SpaM), a bypassing branch for spatial context by retaining the input dimension and constantly communicating its spatial context and rich semantic information mutually with the backbone network.

Image Classification Segmentation +2

Detector With Focus: Normalizing Gradient In Image Pyramid

no code implementations5 Sep 2019 Yonghyun Kim, Bong-Nam Kang, Daijin Kim

An image pyramid can extend many object detection algorithms to solve detection on multiple scales.

object-detection Object Detection +2

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

no code implementations ICCV 2019 Bong-Nam Kang, Yonghyun Kim, Bongjin Jun, Daijin Kim

In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores.

Face Identification Face Recognition +4

Pairwise Relational Networks using Local Appearance Features for Face Recognition

no code implementations15 Nov 2018 Bong-Nam Kang, Yonghyun Kim, Daijin Kim

We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures unique pairwise relations with the same identity and discriminative pairwise relations between different identities.

Face Identification Face Recognition +1

BAN: Focusing on Boundary Context for Object Detection

no code implementations13 Nov 2018 Yonghyun Kim, Taewook Kim, Bong-Nam Kang, Jieun Kim, Daijin Kim

To verify our method, we visualize the activation of the sub-networks according to the boundary contexts and empirically show that the sub-networks contribute more to the related sub-problem in detection.

Object object-detection +1

Pairwise Relational Networks for Face Recognition

no code implementations ECCV 2018 Bong-Nam Kang, Yonghyun Kim, Daijin Kim

Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN.

Face Identification Face Recognition +1

SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

no code implementations ECCV 2018 Yonghyun Kim, Bong-Nam Kang, Daijin Kim

However, due to the lack of scale normalization in CNN-based detection methods, the activated channels in the feature space can be completely different according to a scale and this difference makes it hard for the classifier to learn samples.

object-detection Object Detection

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