Search Results for author: Sunghwan Hong

Found 5 papers, 4 papers with code

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

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

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

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