Search Results for author: Sunghwan Hong

Found 13 papers, 9 papers with code

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

DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation

1 code implementation23 May 2023 Susung Hong, Junyoung Seo, Heeseong Shin, Sunghwan Hong, Seungryong Kim

In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) generation.

Text-to-Video Generation Video Generation +1

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

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

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

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

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.

 Ranked #1 on Dense Pixel Correspondence Estimation on HPatches (using extra training data)

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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