336 citations · 854 across the 53 of their papers we have counts for
11 papers · 1 filter
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Dahun Kim, Donghyeon Cho, In So Kweon
Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images i…
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…
LinkNet: Relational Embedding for Scene Graph
Sanghyun Woo, Dahun Kim, Donghyeon Cho +1
Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However,…
CBAM: Convolutional Block Attention Module
Sanghyun Woo, Jongchan Park, Joon-Young Lee +1
We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, o…
BAM: Bottleneck Attention Module
Jongchan Park, Sanghyun Woo, Joon-Young Lee +1
Recent advances in deep neural networks have been developed via architecture search for stronger representational power. In this work, we focus on the effect of attention in genera…
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning
Jongchan Park, Joon-Young Lee, Donggeun Yoo +1
Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images…
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images
Changha Shin, Hae-Gon Jeon, Youngjin Yoon +2
Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lig…
Robust Depth Estimation from Auto Bracketed Images
Sunghoon Im, Hae-Gon Jeon, In So Kweon
As demand for advanced photographic applications on hand-held devices grows, these electronics require the capture of high quality depth. However, under low-light conditions, most…
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…
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh +2
Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning ha…
Learning Image Representations by Completing Damaged Jigsaw Puzzles
Dahun Kim, Donghyeon Cho, Donggeun Yoo +1
In this paper, we explore methods of complicating self-supervised tasks for representation learning. That is, we do severe damage to data and encourage a network to recover them. F…