25 citations · 40 across the 7 of their papers we have counts for
7 papers
Small Objects Matters in Weakly-supervised Semantic Segmentation
Cheolhyun Mun, Sanghuk Lee, Youngjung Uh +2
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training. Despite the difficulty of this task, the research comm…
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations
Seogkyu Jeon, Bei Liu, Pilhyeon Lee +3
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 gene…
AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks
Kibeom Hong, Seogkyu Jeon, Junsoo Lee +6
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 corre…
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection
Pilhyeon Lee, Taeoh Kim, Minho Shim +2
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…
Source-free Subject Adaptation for EEG-based Visual Recognition
Pilhyeon Lee, Seogkyu Jeon, Sunhee Hwang +2
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 sampl…
Exploiting Shape Cues for Weakly Supervised Semantic Segmentation
Sungpil Kho, Pilhyeon Lee, Wonyoung Lee +2
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 commo…