Search Results for author: Seokju Cho

Found 15 papers, 10 papers with code

Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification

no code implementations23 Jun 2024 Inès Hyeonsu Kim, Joungbin Lee, Soowon Son, Woojeong Jin, Kyusun Cho, Junyoung Seo, Min-Seop Kwak, Seokju Cho, JeongYeol Baek, Byeongwon Lee, Seungryong Kim

We propose Diff-ID, a novel data augmentation approach that incorporates sparse and underrepresented human pose and camera viewpoint examples into the training data, addressing the limited diversity in the original training data distribution.

Data Augmentation Diversity +1

Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence

no code implementations17 Mar 2024 Sunghwan Hong, Seokju Cho, Seungryong Kim, Stephen Lin

In this work, we first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes.

Geometric Matching

FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking

no code implementations CVPR 2024 Seokju Cho, Jiahui Huang, Seungryong Kim, Joon-Young Lee

In the domain of video tracking existing methods often grapple with a trade-off between spatial density and temporal range.

Optical Flow Estimation

DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

1 code implementation30 May 2023 Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho, Min-Seop Kwak, SungJin Cho, Seungryong Kim

Employing monocular depth estimation (MDE) networks, pretrained on large-scale RGB-D datasets, with powerful generalization capability would be a key to solving this problem: however, using MDE in conjunction with NeRF comes with a new set of challenges due to various ambiguity problems exhibited by monocular depths.

Monocular Depth Estimation Novel View Synthesis

DiffFace: Diffusion-based Face Swapping with Facial Guidance

1 code implementation27 Dec 2022 Kihong Kim, Yunho Kim, Seokju Cho, Junyoung Seo, Jisu Nam, Kychul Lee, Seungryong Kim, Kwanghee Lee

In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending.

Face Swapping

Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

1 code implementation6 Oct 2022 Sunghwan Hong, Jisu Nam, Seokju Cho, Susung Hong, Sangryul Jeon, Dongbo Min, Seungryong Kim

Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.

Semantic correspondence

MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation

1 code implementation22 Sep 2022 Junyoung Seo, Gyuseong Lee, Seokju Cho, Jiyoung Lee, Seungryong Kim

Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image.

Denoising Translation

Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence

no code implementations19 Sep 2022 Sunghwan Hong, Seokju Cho, Seungryong Kim, Stephen Lin

The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggregation.

Geometric Matching Semantic correspondence

LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

1 code implementation CVPR 2023 JiHye Park, Sunwoo Kim, Soohyun Kim, Seokju Cho, Jaejun Yoo, Youngjung Uh, Seungryong Kim

Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image.

Translation Unsupervised Image-To-Image Translation

Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation

1 code implementation22 Jul 2022 Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, Seungryong Kim

However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges and decreases inductive bias.

Decoder Few-Shot Semantic Segmentation +2

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.

Pseudo Label

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.

Decoder Few-Shot Semantic Segmentation +3

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

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