31 citations · 183 across the 21 of their papers we have counts for
22 papers
Weakly Supervised Semantic Segmentation for Driving Scenes
Dongseob Kim, Seungho Lee, Junsuk Choe +1
State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) using image-level labels exhibit severe performance degradation on driving scene datasets such as City…
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data
Kyungjune Baek, Hyunjung Shim
Transfer learning for GANs successfully improves generation performance under low-shot regimes. However, existing studies show that the pretrained model using a single benchmark da…
Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds
Minhyun Lee, Dongseob Kim, Hyunjung Shim
Weakly-supervised semantic segmentation (WSSS) has recently gained much attention for its promise to train segmentation models only with image-level labels. Existing WSSS methods c…
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Minjin Choi, Jinhong Kim, Joonsek Lee +2
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-sess…
Few-shot Font Generation with Weakly Supervised Localized Representations
Song Park, Sanghyuk Chun, Junbum Cha +2
Automatic few-shot font generation aims to solve a well-defined, real-world problem because manual font designs are expensive and sensitive to the expertise of designers. Existing…
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation
Seungho Lee, Minhyun Lee, Jongwuk Lee +1
Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries…
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Song Park, Sanghyuk Chun, Junbum Cha +2
A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the…
Session-aware Linear Item-Item Models for Session-based Recommendation
Minjin Choi, jinhong Kim, Joonseok Lee +2
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Spec…
Few-shot Font Generation with Localized Style Representations and Factorization
Song Park, Sanghyuk Chun, Junbum Cha +2
Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot fon…
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
Junsuk Choe, Seong Joon Oh, Sanghyuk Chun +3
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…
Evaluating Weakly Supervised Object Localization Methods Right
Junsuk Choe, Seong Joon Oh, Seungho Lee +3
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal…
Collaborative Distillation for Top-N Recommendation
Jae-woong Lee, Minjin Choi, Jongwuk Lee +1
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model. Despite the success of KD in the…
Attention-based Dropout Layer for Weakly Supervised Object Localization
Junsuk Choe, Hyunjung Shim
Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniq…
Depth Reconstruction of Translucent Objects from a Single Time-of-Flight Camera using Deep Residual Networks
Seongjong Song, Hyunjung Shim
We propose a novel approach to recovering the translucent objects from a single time-of-flight (ToF) depth camera using deep residual networks. When recording the translucent objec…
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously
Kyungjune Baek, Duhyeon Bang, Hyunjung Shim
We propose a novel framework for simultaneously generating and manipulating the face images with desired attributes. While the state-of-the-art attribute editing technique has achi…
Generative Adversarial Networks for Unsupervised Object Co-localization
Junsuk Choe, Joo Hyun Park, Hyunjung Shim
This paper introduces a novel approach for unsupervised object co-localization using Generative Adversarial Networks (GANs). GAN is a powerful tool that can implicitly learn unknow…
Resembled Generative Adversarial Networks: Two Domains with Similar Attributes
Duhyeon Bang, Hyunjung Shim
We propose a novel algorithm, namely Resembled Generative Adversarial Networks (GAN), that generates two different domain data simultaneously where they resemble each other. Althou…
Discriminator Feature-based Inference by Recycling the Discriminator of GANs
Duhyeon Bang, Seoungyoon Kang, Hyunjung Shim
Generative adversarial networks (GANs)successfully generate high quality data by learning amapping from a latent vector to the data. Various studies assert that the latent space of…
MGGAN: Solving Mode Collapse using Manifold Guided Training
Duhyeon Bang, Hyunjung Shim
Mode collapse is a critical problem in training generative adversarial networks. To alleviate mode collapse, several recent studies introduce new objective functions, network archi…
Improved Techniques For Weakly-Supervised Object Localization
Junsuk Choe, Joo Hyun Park, Hyunjung Shim
We propose an improved technique for weakly-supervised object localization. Conventional methods have a limitation that they focus only on most discriminative parts of the target o…
Improved Training of Generative Adversarial Networks Using Representative Features
Duhyeon Bang, Hyunjung Shim
Despite the success of generative adversarial networks (GANs) for image generation, the trade-off between visual quality and image diversity remains a significant issue. This paper…