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pro vyhledávání: '"Kim, Seung Hwan"'
Autor:
Ryoo, Kwangrok, Jo, Yeonsik, Lee, Seungjun, Kim, Mira, Jo, Ahra, Kim, Seung Hwan, Kim, Seungryong, Lee, Soonyoung
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes. However, previous studies have only addres
Externí odkaz:
http://arxiv.org/abs/2312.13822
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-efficiency, recent efforts have introduced additional supervision terms tha
Externí odkaz:
http://arxiv.org/abs/2312.12661
Recent advances in vision language pretraining (VLP) have been largely attributed to the large-scale data collected from the web. However, uncurated dataset contains weakly correlated image-text pairs, causing data inefficiency. To address the issue,
Externí odkaz:
http://arxiv.org/abs/2312.12659
Autor:
Kim, Taehoon, Ahn, Pyunghwan, Kim, Sangyun, Lee, Sihaeng, Marsden, Mark, Sala, Alessandra, Kim, Seung Hwan, Han, Bohyung, Lee, Kyoung Mu, Lee, Honglak, Bae, Kyounghoon, Wu, Xiangyu, Gao, Yi, Zhang, Hailiang, Yang, Yang, Guo, Weili, Lu, Jianfeng, Oh, Youngtaek, Cho, Jae Won, Kim, Dong-jin, Kweon, In So, Kim, Junmo, Kang, Wooyoung, Jhoo, Won Young, Roh, Byungseok, Mun, Jonghwan, Oh, Solgil, Ak, Kenan Emir, Lee, Gwang-Gook, Xu, Yan, Shen, Mingwei, Hwang, Kyomin, Shin, Wonsik, Lee, Kamin, Park, Wonhark, Lee, Dongkwan, Kwak, Nojun, Wang, Yujin, Wang, Yimu, Gu, Tiancheng, Lv, Xingchang, Sun, Mingmao
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image capt
Externí odkaz:
http://arxiv.org/abs/2309.01961
Autor:
Ahn, Daechul, Kim, Daneul, Song, Gwangmo, Kim, Seung Hwan, Lee, Honglak, Kang, Dongyeop, Choi, Jonghyun
Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-term context across multiple sentences. While prior efforts mostly foc
Externí odkaz:
http://arxiv.org/abs/2308.07575
Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is co
Externí odkaz:
http://arxiv.org/abs/2305.16713
Deep neural networks have been successfully adopted to diverse domains including pathology classification based on medical images. However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labe
Externí odkaz:
http://arxiv.org/abs/2212.07050
Autor:
Kim, Taehoon, Marsden, Mark, Ahn, Pyunghwan, Kim, Sangyun, Lee, Sihaeng, Sala, Alessandra, Kim, Seung Hwan
When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strateg
Externí odkaz:
http://arxiv.org/abs/2211.06774
Autor:
Lee, Janghyeon, Kim, Jongsuk, Shon, Hyounguk, Kim, Bumsoo, Kim, Seung Hwan, Lee, Honglak, Kim, Junmo
Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream applications. Some following works have targeted to improve data effici
Externí odkaz:
http://arxiv.org/abs/2209.13430
This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dat
Externí odkaz:
http://arxiv.org/abs/2209.02239