336 citations · 516 across the 24 of their papers we have counts for
28 papers
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Sanghyun Woo, Kwanyong Park, Inkyu Shin +2
Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. This task has practical…
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
Daehan Kim, Minseok Seo, Kwanyong Park +4
Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain…
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu +4
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2…
Tracking by Associating Clips
Sanghyun Woo, Kwanyong Park, Seoung Wug Oh +2
The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association…
Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection
Sanghyun Woo, Kwanyong Park, Seoung Wug Oh +2
Scaling object taxonomies is one of the important steps toward a robust real-world deployment of recognition systems. We have faced remarkable progress in images since the introduc…
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
Sungsu Hur, Inkyu Shin, Kwanyong Park +2
Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguish…
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management
Junha Song, Kwanyong Park, InKyu Shin +3
Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allo…
Per-Clip Video Object Segmentation
Kwanyong Park, Sanghyun Woo, Seoung Wug Oh +2
Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently…
Talking Face Generation with Multilingual TTS
Hyoung-Kyu Song, Sang Hoon Woo, Junhyeok Lee +5
In this work, we propose a joint system combining a talking face generation system with a text-to-speech system that can generate multilingual talking face videos from only the tex…
Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation
KwanYong Park, Sanghyun Woo, Inkyu Shin +1
Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently, as it could be beneficial for various label-scarce real-world scenarios (e.g.…
Studying the Effects of Self-Attention for Medical Image Analysis
Adrit Rao, Jongchan Park, Sanghyun Woo +2
When the trained physician interprets medical images, they understand the clinical importance of visual features. By applying cognitive attention, they apply greater focus onto cli…
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Inkyu Shin, Dong-jin Kim, Jae Won Cho +3
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despit…
Unsupervised Domain Adaptation for Video Semantic Segmentation
Inkyu Shin, Kwanyong Park, Sanghyun Woo +1
Unsupervised Domain Adaptation for semantic segmentation has gained immense popularity since it can transfer knowledge from simulation to real (Sim2Real) by largely cutting out the…
Learning to Associate Every Segment for Video Panoptic Segmentation
Sanghyun Woo, Dahun Kim, Joon-Young Lee +1
Temporal correspondence - linking pixels or objects across frames - is a fundamental supervisory signal for the video models. For the panoptic understanding of dynamic scenes, we f…
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation
Inkyu Shin, Sanghyun Woo, Fei Pan +1
Recently, deep self-training approaches emerged as a powerful solution to the unsupervised domain adaptation. The self-training scheme involves iterative processing of target data;…
The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation
Myungchul Kim, Sanghyun Woo, Dahun Kim +1
Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more…
Video Panoptic Segmentation
Dahun Kim, Sanghyun Woo, Joon-Young Lee +1
Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we…
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
Yunjae Jung, Dahun Kim, Sanghyun Woo +3
Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but a…
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…
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…
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…
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…
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…
Discriminative Feature Learning for Unsupervised Video Summarization
Yunjae Jung, Donghyeon Cho, Dahun Kim +2
In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues bas…