46 citations · 279 across the 39 of their papers we have counts for
8 papers · 1 filter
Listen to Look: Action Recognition by Previewing Audio
Ruohan Gao, Tae-Hyun Oh, Kristen Grauman +1
In the face of the video data deluge, today's expensive clip-level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed vi…
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…
Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh +1
Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand…
Speech2Face: Learning the Face Behind a Voice
Tae-Hyun Oh, Tali Dekel, Changil Kim +4
How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of…
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…
Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh +1
Our goal in this work is to train an image captioning model that generates more dense and informative captions. We introduce "relational captioning," a novel image captioning task…
Neural Inverse Knitting: From Images to Manufacturing Instructions
Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura +3
Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a…