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20172023
most citedLearning JPEG Compression Artifacts for Image Manipulation Detection and Localization

214 citations · 712 across the 42 of their papers we have counts for

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Showing 2019Show all

14 papers · 1 filter

cs.CV2019★ 3 cited

Learning to Discriminate Information for Online Action Detection

Hyunjun Eun, Jinyoung Moon, Jongyoul Park +2

From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of cu…

cs.CV2019★ 25 cited

Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification

Seokeon Choi, Sumin Lee, Youngeun Kim +2

Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance in…

cs.CV2019★ 29 cited

SRG: Snippet Relatedness-based Temporal Action Proposal Generator

Hyunjun Eun, Sumin Lee, Jinyoung Moon +3

Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate b…

eess.IV2019

BitNet: Learning-Based Bit-Depth Expansion

Junyoung Byun, Kyujin Shim, Changick Kim

Bit-depth is the number of bits for each color channel of a pixel in an image. Although many modern displays support unprecedented higher bit-depth to show more realistic and natur…

cs.CV2019

RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation

Youngeun Kim, Seokeon Choi, Hankyeol Lee +2

In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data.Specifically, we present Robust Pixel-level Matching Net-works (RPM-…

cs.CV2019

CNN-based Semantic Segmentation using Level Set Loss

Youngeun Kim, Seunghyeon Kim, Taekyung Kim +1

Thesedays, Convolutional Neural Networks are widely used in semantic segmentation. However, since CNN-based segmentation networks produce low-resolution outputs with rich semantic…