Search Results for author: Seungryong Kim

Found 37 papers, 13 papers with code

Guided Semantic Flow

no code implementations ECCV 2020 Sangryul Jeon, Dongbo Min, Seungryong Kim, Jihwan Choe, Kwanghoon Sohn

Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images.

Semantic correspondence

AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object Manipulation

no code implementations28 Apr 2022 Mira Kim, Jaehoon Ko, Kyusun Cho, Junmyeong Choi, Daewon Choi, Seungryong Kim

We propose a novel framework for 3D-aware object manipulation, called Auto-Encoding Neural Radiance Fields (AE-NeRF).


Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence

no code implementations5 Apr 2022 Jiwon Kim, Youngjo Min, Mira Kim, Seungryong Kim

In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence.

Semantic correspondence

InstaFormer: Instance-Aware Image-to-Image Translation with Transformer

no code implementations30 Mar 2022 Soohyun Kim, Jongbeom Baek, JiHye Park, Gyeongnyeon Kim, Seungryong Kim

By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness.

Image-to-Image Translation Translation

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

no code implementations30 Mar 2022 Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch.

Data Augmentation Semantic correspondence

CATs++: Boosting Cost Aggregation with Convolutions and Transformers

1 code implementation14 Feb 2022 Seokju Cho, Sunghwan Hong, Seungryong Kim

Cost aggregation is a highly important process in image matching tasks, which aims to disambiguate the noisy matching scores.

Semantic correspondence

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

no code implementations25 Jan 2022 Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee, Seokju Cho, Junyoung Seo, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.

Memory-guided Image De-raining Using Time-Lapse Data

no code implementations6 Jan 2022 Jaehoon Cho, Seungryong Kim, Kwanghoon Sohn

To address this problem, we propose a novel network architecture based on a memory network that explicitly helps to capture long-term rain streak information in the time-lapse data.

Cost Aggregation Is All You Need for Few-Shot Segmentation

2 code implementations22 Dec 2021 Sunghwan Hong, Seokju Cho, Jisu Nam, Seungryong Kim

We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.

Few-Shot Semantic Segmentation Semantic correspondence

Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

1 code implementation16 Dec 2021 Yoonna Jang, Jungwoo Lim, Yuna Hur, Dongsuk Oh, Suhyune Son, Yeonsoo Lee, Donghoon Shin, Seungryong Kim, Heuiseok Lim

Humans usually have conversations by making use of prior knowledge about a topic and background information of the people whom they are talking to.

Deep Translation Prior: Test-time Training for Photorealistic Style Transfer

1 code implementation12 Dec 2021 Sunwoo Kim, Soohyun Kim, Seungryong Kim

Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles.

Style Transfer Translation

Learning Canonical 3D Object Representation for Fine-Grained Recognition

no code implementations ICCV 2021 Sunghun Joung, Seungryong Kim, Minsu Kim, Ig-Jae Kim, Kwanghoon Sohn

By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification.

3D Shape Reconstruction Fine-Grained Image Recognition +2

Deep Matching Prior: Test-Time Optimization for Dense Correspondence

1 code implementation ICCV 2021 Sunghwan Hong, Seungryong Kim

Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model.

Dense Pixel Correspondence Estimation Geometric Matching

CATs: Cost Aggregation Transformers for Visual Correspondence

1 code implementation NeurIPS 2021 Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim

We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.

Semantic correspondence

Modeling Object Dissimilarity for Deep Saliency Prediction

no code implementations8 Apr 2021 Bahar Aydemir, Deblina Bhattacharjee, Seungryong Kim, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level one, such as attention and gaze direction for entire objects.

Saliency Prediction

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

3 code implementations CVPR 2021 Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne Kim, Seungryong Kim, Jaegul Choo

Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving.

Autonomous Driving Domain Generalization +1

On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

1 code implementation2 Jan 2021 Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, Stefano Mattoccia

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images.

Stereo Matching

Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation

1 code implementation15 Dec 2020 Minsu Kim, Sunghun Joung, Seungryong Kim, Jungin Park, Ig-Jae Kim, Kwanghoon Sohn

Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner.

Domain Adaptation Semantic Segmentation

Online Exemplar Fine-Tuning for Image-to-Image Translation

no code implementations18 Nov 2020 Taewon Kang, Soohyun Kim, Sunwoo Kim, Seungryong Kim

Existing techniques to solve exemplar-based image-to-image translation within deep convolutional neural networks (CNNs) generally require a training phase to optimize the network parameters on domain-specific and task-specific benchmarks, thus having limited applicability and generalization ability.

Image-to-Image Translation Translation

Adaptive confidence thresholding for monocular depth estimation

no code implementations ICCV 2021 Hyesong Choi, Hunsang Lee, Sunkyung Kim, Sunok Kim, Seungryong Kim, Kwanghoon Sohn, Dongbo Min

To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps.

Monocular Depth Estimation Stereo Matching

Volumetric Transformer Networks

no code implementations ECCV 2020 Seungryong Kim, Sabine Süsstrunk, Mathieu Salzmann

We design our VTN as an encoder-decoder network, with modules dedicated to letting the information flow across the feature channels, to account for the dependencies between the semantic parts.

Fine-Grained Image Recognition Image Retrieval

Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation

no code implementations CVPR 2020 Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim, Ig-Jae Kim, Junghyun Cho, Kwanghoon Sohn

To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space.

Object Detection Viewpoint Estimation

Joint Learning of Semantic Alignment and Object Landmark Detection

no code implementations ICCV 2019 Sangryul Jeon, Dongbo Min, Seungryong Kim, Kwanghoon Sohn

Based on the key insight that the two tasks can mutually provide supervisions to each other, our networks accomplish this through a joint loss function that alternatively imposes a consistency constraint between the two tasks, thereby boosting the performance and addressing the lack of training data in a principled manner.

Context-Aware Emotion Recognition Networks

1 code implementation ICCV 2019 Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, Kwanghoon Sohn

We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner.

Emotion Recognition in Context

Semantic Attribute Matching Networks

no code implementations CVPR 2019 Seungryong Kim, Dongbo Min, Somi Jeong, Sunok Kim, Sangryul Jeon, Kwanghoon Sohn

SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences.

Recurrent Transformer Networks for Semantic Correspondence

1 code implementation NeurIPS 2018 Seungryong Kim, Stephen Lin, Sangryul Jeon, Dongbo Min, Kwanghoon Sohn

Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations.

General Classification Semantic correspondence

PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence

no code implementations ECCV 2018 Sangryul Jeon, Seungryong Kim, Dongbo Min, Kwanghoon Sohn

To the best of our knowledge, it is the first work that attempts to estimate dense affine transformation fields in a coarse-to-fine manner within deep networks.

Semantic correspondence

DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

no code implementations ICCV 2017 Seungryong Kim, Dongbo Min, Stephen Lin, Kwanghoon Sohn

In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor.

Semantic correspondence

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

1 code implementation CVPR 2017 Seungryong Kim, Dongbo Min, Bumsub Ham, Sangryul Jeon, Stephen Lin, Kwanghoon Sohn

The sampling patterns of local structure and the self-similarity measure are jointly learned within the proposed network in an end-to-end and multi-scale manner.

Semantic correspondence

DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation

no code implementations27 Apr 2016 Seungryong Kim, Dongbo Min, Bumsub Ham, Minh N. Do, Kwanghoon Sohn

In this paper, we propose a novel dense descriptor, called dense adaptive self-correlation (DASC), to estimate multi-modal and multi-spectral dense correspondences.

Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence

no code implementations21 Mar 2016 Seungryong Kim, Dongbo Min, Stephen Lin, Kwanghoon Sohn

We present a novel descriptor, called deep self-convolutional activations (DeSCA), designed for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions.

DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence

no code implementations CVPR 2015 Seungryong Kim, Dongbo Min, Bumsub Ham, Seungchul Ryu, Minh N. Do, Kwanghoon Sohn

To further improve the matching quality and runtime efficiency, we propose a patch-wise receptive field pooling, in which a sampling pattern is optimized with a discriminative learning.

Optical Flow Estimation

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