no code implementations • 9 Jan 2023 • Judith Yue Li, Aren Jansen, Qingqing Huang, Joonseok Lee, Ravi Ganti, Dima Kuzmin
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
no code implementations • 9 Oct 2022 • Yeji Song, Chaerin Kong, Seoyoung Lee, Nojun Kwak, Joonseok Lee
Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects.
no code implementations • 26 Aug 2022 • Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, Daniel P. W. Ellis
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
1 code implementation • 26 Jul 2022 • Jae-woong Lee, Seongmin Park, Joonseok Lee, Jongwuk Lee
Implicit feedback has been widely used to build commercial recommender systems.
no code implementations • CVPR 2022 • Dayoung Gong, Joonseok Lee, Manjin Kim, Seong Jong Ha, Minsu Cho
The task of predicting future actions from a video is crucial for a real-world agent interacting with others.
1 code implementation • 15 Apr 2022 • Hyungyung Lee, Sungjin Park, Joonseok Lee, Edward Choi
To learn a multimodal semantic correlation in a quantized space, we combine VQ-VAE with a Transformer encoder and apply an input masking strategy.
1 code implementation • 14 Jan 2022 • Jonghwan Mun, Minchul Shin, Gunsoo Han, Sangho Lee, Seongsu Ha, Joonseok Lee, Eun-Sol Kim
Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.
1 code implementation • CVPR 2022 • Myeongho Jeon, Daekyung Kim, Woochul Lee, Myungjoo Kang, Joonseok Lee
Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data.
no code implementations • 21 Dec 2021 • Kangyeol Kim, Sunghyun Park, Junsoo Lee, Joonseok Lee, Sookyung Kim, Jaegul Choo, Edward Choi
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
2 code implementations • 1 Dec 2021 • Woncheol Shin, Gyubok Lee, Jiyoung Lee, Joonseok Lee, Edward Choi
Recently, vector-quantized image modeling has demonstrated impressive performance on generation tasks such as text-to-image generation.
no code implementations • 29 Sep 2021 • Jonghwan Mun, Minchul Shin, Gunsoo Han, Sangho Lee, Seongsu Ha, Joonseok Lee, Eun-Sol Kim
Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.
3 code implementations • 30 Mar 2021 • Minjin Choi, jinhong Kim, Joonseok Lee, Hyunjung Shim, Jongwuk Lee
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.
1 code implementation • 30 Mar 2021 • Minjin Choi, Yoonki Jeong, Joonseok Lee, Jongwuk Lee
Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results.
no code implementations • 18 Nov 2020 • BoWen Zhang, Hexiang Hu, Joonseok Lee, Ming Zhao, Sheide Chammas, Vihan Jain, Eugene Ie, Fei Sha
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.
1 code implementation • 16 Oct 2020 • Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, Edward Choi
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
no code implementations • 3 Jan 2020 • Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton
Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect.
Computers and Society
no code implementations • 8 Oct 2019 • José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti. R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, Joonho Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
1 code implementation • 24 Feb 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Ranked #41 on
Node Classification
on Pubmed
no code implementations • ICLR 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.
6 code implementations • 27 Sep 2016 • Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on
Action Recognition In Videos
on ActivityNet
no code implementations • 19 Apr 2013 • Joonseok Lee, Kisung Lee, Jennifer G. Kim
To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him.
no code implementations • NeurIPS 2012 • Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon
Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings.