Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Byun, Hyeran"'
Autor:
Lee, Pilhyeon, Byun, Hyeran
Temporal sentence grounding aims to localize moments relevant to a language description. Recently, DETR-like approaches achieved notable progress by predicting the center and length of a target moment. However, they suffer from the issue of center mi
Externí odkaz:
http://arxiv.org/abs/2312.00083
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training. Despite the difficulty of this task, the research community has achieved promising results over the last five years. Still,
Externí odkaz:
http://arxiv.org/abs/2309.14117
Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any fur
Externí odkaz:
http://arxiv.org/abs/2308.10554
Autor:
Hong, Kibeom, Jeon, Seogkyu, Lee, Junsoo, Ahn, Namhyuk, Kim, Kunhee, Lee, Pilhyeon, Kim, Daesik, Uh, Youngjung, Byun, Hyeran
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low sem
Externí odkaz:
http://arxiv.org/abs/2307.09724
Temporal action detection aims to predict the time intervals and the classes of action instances in the video. Despite the promising performance, existing two-stream models exhibit slow inference speed due to their reliance on computationally expensi
Externí odkaz:
http://arxiv.org/abs/2303.17285
Autor:
Shin, Minjung, Seo, Yunji, Bae, Jeongmin, Choi, Young Sun, Kim, Hyunsu, Byun, Hyeran, Uh, Youngjung
3D-aware GANs aim to synthesize realistic 3D scenes such that they can be rendered in arbitrary perspectives to produce images. Although previous methods produce realistic images, they suffer from unstable training or degenerate solutions where the 3
Externí odkaz:
http://arxiv.org/abs/2301.09091
This paper focuses on subject adaptation for EEG-based visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited, by transferring knowledge from abundant data of source
Externí odkaz:
http://arxiv.org/abs/2301.08448
Publikováno v:
Pattern Recognition 132 (2022): 108953
Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. To this end, previous methods adopt the common pipeline: they generate pseudo masks from class activation maps (CAM
Externí odkaz:
http://arxiv.org/abs/2208.04286
Publikováno v:
Expert Systems with Applications 205 (2022): 117697
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial level
Externí odkaz:
http://arxiv.org/abs/2207.09613
Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon outperformed the dominant methods based on
Externí odkaz:
http://arxiv.org/abs/2203.16209