29 citations · 75 across the 16 of their papers we have counts for
16 papers · 1 filter
Sound Source Localization is All about Cross-Modal Alignment
Arda Senocak, Hyeonggon Ryu, Junsik Kim +3
Humans can easily perceive the direction of sound sources in a visual scene, termed sound source localization. Recent studies on learning-based sound source localization have mainl…
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation
Moon Ye-Bin, Dongmin Choi, Yongjin Kwon +2
We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem…
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Fei Pan, Sungsu Hur, Seokju Lee +2
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap betwee…
Audio-Visual Fusion Layers for Event Type Aware Video Recognition
Arda Senocak, Junsik Kim, Tae-Hyun Oh +3
Human brain is continuously inundated with the multisensory information and their complex interactions coming from the outside world at any given moment. Such information is automa…
Learning Sound Localization Better From Semantically Similar Samples
Arda Senocak, Hyeonggon Ryu, Junsik Kim +1
The objective of this work is to localize the sound sources in visual scenes. Existing audio-visual works employ contrastive learning by assigning corresponding audio-visual pairs…
Optical Flow Estimation from a Single Motion-blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +2
In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in…
Motion-blurred Video Interpolation and Extrapolation
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +1
Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and tempo…
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…
Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Arda Senocak, Tae-Hyun Oh, Junsik Kim +2
Visual events are usually accompanied by sounds in our daily lives. However, can the machines learn to correlate the visual scene and sound, as well as localize the sound source on…
Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hyun Oh +4
Dashboard cameras capture a tremendous amount of driving scene video each day. These videos are purposefully coupled with vehicle sensing data, such as from the speedometer and ine…
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Junsik Kim, Tae-Hyun Oh, Seokju Lee +2
In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an op…
Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim +2
Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize…
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Junsik Kim, Seokju Lee, Tae-Hyun Oh +1
Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data. However,the acquisition of a large supervised dataset is often challen…
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee, Junsik Kim, Jae Shin Yoon +7
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point…
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Jae Shin Yoon, Francois Rameau, Junsik Kim +3
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from…
Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim +1
Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by the Manhattan world…