no code implementations • 13 Apr 2018 • Jungbeom Lee, Jangho Lee, Sungmin Lee, Sungroh Yoon
Video prediction can be performed by finding features in recent frames, and using them to generate approximations to upcoming frames.
Ranked #1 on Video Prediction on KTH (Cond metric)
no code implementations • 29 May 2018 • Sungmin Lee, Jangho Lee, Jungbeom Lee, Chul-Kee Park, Sungroh Yoon
There have been various studies concerning automated lesion detection.
no code implementations • CVPR 2019 • Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon
The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations.
no code implementations • ICCV 2019 • Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon
We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image.
1 code implementation • CVPR 2021 • Jungbeom Lee, Jihun Yi, Chaehun Shin, Sungroh Yoon
Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object.
1 code implementation • CVPR 2021 • Jungbeom Lee, Eunji Kim, Sungroh Yoon
Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target object.
2 code implementations • ICCV 2021 • Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong, Sungroh Yoon
From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased.
1 code implementation • NeurIPS 2021 • Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon
Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object.
Ranked #18 on Weakly-Supervised Semantic Segmentation on COCO 2014 val
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
1 code implementation • CVPR 2022 • Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, Sungroh Yoon
However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e. g. train and rail), fundamentally bounding the performance of WSSS.
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
5 code implementations • CVPR 2022 • Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon
Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.
no code implementations • CVPR 2022 • Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, Sungroh Yoon
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels.
no code implementations • 11 Apr 2022 • Jungbeom Lee, Eunji Kim, Jisoo Mok, Sungroh Yoon
This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack.
no code implementations • ICCV 2023 • Jungbeom Lee, Sungjin Lee, Jinseok Nam, Seunghak Yu, Jaeyoung Do, Tara Taghavi
Referring image segmentation (RIS) aims to localize the object in an image referred by a natural language expression.
1 code implementation • 8 Jun 2023 • Seungryong Yoo, Eunji Kim, Dahuin Jung, Jungbeom Lee, Sungroh Yoon
Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks.
Ranked #2 on Visual Prompt Tuning on VTAB-1k(Natural<7>)
no code implementations • 27 Mar 2024 • Jungbeom Lee, Sanghyuk Chun, Sangdoo Yun
Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements.