Namyup Kim

I am a Ph.D. student in Computer Science Engineering department at POSTECH, working as a member of the Computer Vision Lab, advised by Prof. Suha Kwak.

My research lies in computer vision and deep learning including, but not limited to, multimodal learning, domain generalization, weakly supervised learning and its applications. I am particularly interested in referring image segmentation which is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. If you are interested in my research projects, please feel free to contact me.

News

Mar 1, 2023	📜 A paper on cross-modal retrieval is accepted to CVPR 2023.
Feb 1, 2023	📜 A paper on domain generalization for semantic segmentation is accepted to ICRA 2023.
Nov 21, 2022	🎉 I won the NAVER Ph.D. Fellowship 2022.
Nov 7, 2022	🎉 I won the Qualcomm Innovation Fellowship 2022.
Jun 27, 2022	📜 A paper on weakly supervised learning for semantic boundary detection is accepted to IJCV.
Jun 20, 2022	📜 Two papers on domain generalization and referring image segmentation are accepted to CVPR 2022.
Mar 29, 2022	📰 Our work on referring image segmentation is featured in MSRA Highlighted Research.

Education

Mar, 2022 - Present	Pohang University of Science and Technology (POSTECH), Pohang, South Korea Integrated M.S./Ph.D. student in Computer Science and Engineering Advisor: Prof. Suha Kwak
Mar, 2011 - Feb, 2018	Soongsil University, Seoul, South Korea B.S. in Electronic Engineering Advisor: Prof. Dongsung Kim

Experience

Dec, 2020 - Jun, 2021	Microsoft Research Asia (Remote), Beijing, China Research Intern Researched on domain generalization and referring image segmentation. Mentor: Dr. Cuiling Lan
Mar, 2018 - Present	Computer Vision Lab, POSTECH, Pohang, South Korea Research and Teaching Assistant

Publications

Improving Cross-Modal Retrieval with Set of Diverse Embeddings

Dongwon Kim, Namyup Kim, and Suha Kwak

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

Abstract Paper Website

Cross-modal retrieval across image and text modalities is a challenging task due to its inherent ambiguity: An image often exhibits various situations, and a caption can be coupled with diverse images. Set-based embedding has been studied as a solution to this problem. It seeks to encode a sample into a set of different embedding vectors that capture different semantics of the sample. In this paper, we present a novel set-based embedding method, which is distinct from previous work in two aspects. First, we present a new similarity function called smooth-Chamfer similarity, which is designed to alleviate the side effects of existing similarity functions for set-based embedding. Second, we propose a novel set prediction module to produce a set of embedding vectors that effectively captures diverse semantics of input by the slot attention mechanism. Our method is evaluated on the COCO and Flickr30K datasets across different visual backbones, where it outperforms existing methods including ones that demand substantially larger computation at inference.
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation

Namyup Kim, Taeyoung Son, Jaehyun Pahk, Cuiling Lan, Wenjun Zeng, and Suha Kwak

IEEE International Conference on Robotics and Automation (ICRA), 2023

Abstract Paper

Domain generalization for semantic segmentation is highly demanded in real applications, where a trained model is expected to work well in previously unseen domains. One challenge lies in the lack of data which could cover the diverse distributions of the possible unseen domains for training. In this paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme, which is the first to exploit the diversity of web-crawled images for generalizable semantic segmentation. To explore and exploit the real-world data distributions, we collect a web-crawled dataset which presents large diversity in terms of weather conditions, sites, lighting, camera styles, etc. We also present a method which injects the style representation of the web-crawled data into the source domain on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training. Moreover, we use the web-crawled dataset with predicted pseudo labels for training to further enhance the capability of the network. Extensive experiments demonstrate that our method clearly outperforms existing domain generalization techniques.
ReSTR: Convolution-free Referring Image Segmentation Using Transformers

Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Abstract Paper Website

Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities. To address these issues, we present the first convolution-free model for referring image segmentation using transformers, dubbed ReSTR. Since it extracts features of both modalities through transformer encoders, it can capture long-range dependencies between entities within each modality. Also, ReSTR fuses features of the two modalities by a self-attention encoder, which enables flexible and adaptive interactions between the two modalities in the fusion process. The fused features are fed to a segmentation module, which works adaptively according to the image and language expression in hand. ReSTR is evaluated and compared with previous work on all public benchmarks, where it outperforms all existing models.
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization

Juwon Kang, Sohyun Lee, Namyup Kim, and Suha Kwak

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Abstract Website

This paper studies domain generalization via domain-invariant representation learning. Existing methods in this direction suppose that a domain can be characterized by styles of its images, and train a network using style-augmented data so that the network is not biased to par-ticular style distributions. However, these methods are restricted to a finite set of styles since they obtain styles for augmentation from a fixed set of external images or by interpolating those of training data. To address this limitation and maximize the benefit of style augmentation, we propose a new method that synthesizes novel styles constantly during training. Our method manages multiple queues to store styles that have been observed so far, and synthesizes novel styles whose distribution is distinct from the distribution of styles in the queues. The style synthesis process is formulated as a monotone submodular optimization, thus can be conducted efficiently by a greedy algorithm. Extensive experiments on four public benchmarks demonstrate that the proposed method is capable of achieving state-of-the-art domain generalization performance.
Learning to Detect Semantic Boundaries with Image-Level Class Labels

Namyup Kim*, Sehyun Hwang*, and Suha Kwak (*equal contribution)

International Journal of Computer Vision (IJCV), 2022

Abstract Paper

This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.
Urie: Universal image enhancement for visual recognition in the wild

Taeyoung Son, Juwon Kang, Namyup Kim, Sunghyun Cho, and Suha Kwak

European Conference on Computer Vision (ECCV), 2020

Abstract Paper Code Website

Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world. To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them. URIE is universal in that it aims to handle various factors of image degradation and to be incorporated with any arbitrary recognition models. Also, it is recognition-friendly since it is optimized to improve the robustness of following recognition models, instead of perceptual quality of output image. Our experiments demonstrate that URIE can handle various and latent image distortions and improve the performance of existing models for five diverse recognition tasks when input images are degraded.