10 citations · 33 across the 7 of their papers we have counts for
7 papers
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggr…
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam +2
This paper presents a novel cost aggregation network, called Volumetric Aggregation with Transformers (VAT), for few-shot segmentation. The use of transformers can benefit correlat…
CATs++: Boosting Cost Aggregation with Convolutions and Transformers
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. Existing methods generally tackle this by hand-crafted…
Cost Aggregation Is All You Need for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam +1
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 transf…
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Hojoon Lee, Dongyoon Hwang, Sunghwan Hong +3
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest. Although Transformer-based models achieved state-of-the-art…
Deep Matching Prior: Test-Time Optimization for Dense Correspondence
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…
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon +3
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challeng…