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20172024
most citedCBAM: Convolutional Block Attention Module

336 citations · 516 across the 24 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CV2019★ 32 cited

Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation

Kwanyong Park, Sanghyun Woo, Dahun Kim +2

In this paper, we investigate the problem of unpaired video-to-video translation. Given a video in the source domain, we aim to learn the conditional distribution of the correspond…

cs.CV2019★ 3 cited

Propose-and-Attend Single Shot Detector

Ho-Deok Jang, Sanghyun Woo, Philipp Benz +2

We present a simple yet effective prediction module for a one-stage detector. The main process is conducted in a coarse-to-fine manner. First, the module roughly adjusts the defaul…

cs.CV2019

Align-and-Attend Network for Globally and Locally Coherent Video Inpainting

Sanghyun Woo, Dahun Kim, KwanYong Park +2

We propose a novel feed-forward network for video inpainting. We use a set of sampled video frames as the reference to take visible contents to fill the hole of a target frame. Our…

cs.CV2019★ 2 cited

Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence

Dahun Kim, Sanghyun Woo, Joon-Young Lee +1

Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks. While recent deep learning based…

cs.CV2019

Deep Video Inpainting

Dahun Kim, Sanghyun Woo, Joon-Young Lee +1

Video inpainting aims to fill spatio-temporal holes with plausible content in a video. Despite tremendous progress of deep neural networks for image inpainting, it is challenging t…