122 citations · 253 across the 25 of their papers we have counts for
10 papers · 1 filter
Unsupervised Representation Learning via Neural Activation Coding
Yookoon Park, Sangho Lee, Gunhee Kim +1
We present neural activation coding (NAC) as a novel approach for learning deep representations from unlabeled data for downstream applications. We argue that the deep encoder shou…
Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods
Seohong Park, Jaekyeom Kim, Gunhee Kim
In reinforcement learning, continuous time is often discretized by a time scale , to which the resulting performance is known to be highly sensitive. In this work, we seek to fi…
Continual Learning on Noisy Data Streams via Self-Purified Replay
Chris Dongjoo Kim, Jinseo Jeong, Sangwoo Moon +1
Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a repla-ybased continual…
Pano-AVQA: Grounded Audio-Visual Question Answering on 360 Videos
Heeseung Yun, Youngjae Yu, Wonsuk Yang +2
360 videos convey holistic views for the surroundings of a scene. It provides audio-visual cues beyond pre-determined normal field of views and displays distinctive spatial…
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
Hyunwoo Kim, Byeongchang Kim, Gunhee Kim
Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue…
On the Virality of Animated GIFs on Tumblr
Yunseok Jang, Yale Song, Gunhee Kim
Animated GIFs are becoming increasingly popular in online communication. People use them to express emotion, share their interests and enhance (or even replace) short-form texting;…
Cycled Compositional Learning between Images and Text
Jongseok Kim, Youngjae Yu, Seunghwan Lee +1
We present an approach named the Cycled Composition Network that can measure the semantic distance of the composition of image-text embedding. First, the Composition Network transi…
Unsupervised Skill Discovery with Bottleneck Option Learning
Jaekyeom Kim, Seohong Park, Gunhee Kim
Having the ability to acquire inherent skills from environments without any external rewards or supervision like humans is an important problem. We propose a novel unsupervised ski…
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
Jaekyeom Kim, Minjung Kim, Dongyeon Woo +1
We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable. Drop-Bottleneck not only en…
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning
Sangho Lee, Jiwan Chung, Youngjae Yu +4
The natural association between visual observations and their corresponding sound provides powerful self-supervisory signals for learning video representations, which makes the eve…