Zobrazeno 1 - 10
of 219
pro vyhledávání: '"Park, Chaewon"'
Numerous datasets have been proposed to combat the spread of online hate. Despite these efforts, a majority of these resources are English-centric, primarily focusing on overt forms of hate. This research gap calls for developing high-quality corpora
Externí odkaz:
http://arxiv.org/abs/2310.15439
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided
Externí odkaz:
http://arxiv.org/abs/2303.08314
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to model normal features without seeing abnormal samples during training and
Externí odkaz:
http://arxiv.org/abs/2302.09794
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficulties of the task, COD has gained considerable attention in several fie
Externí odkaz:
http://arxiv.org/abs/2211.12048
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is
Externí odkaz:
http://arxiv.org/abs/2211.07381
Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting features for the most salient common objects within a video sequence. This
Externí odkaz:
http://arxiv.org/abs/2209.03712
Autor:
Cho, Suhwan, Kim, Woo Jin, Cho, MyeongAh, Lee, Seunghoon, Lee, Minhyeok, Park, Chaewon, Lee, Sangyoun
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjective matching is adopted, background distractors can easily occur and de
Externí odkaz:
http://arxiv.org/abs/2209.03139
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical flow maps in addition to appeara
Externí odkaz:
http://arxiv.org/abs/2209.03138
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the l
Externí odkaz:
http://arxiv.org/abs/2207.07898
Autor:
Cho, Suhwan, Lee, Heansung, Lee, Minhyeok, Park, Chaewon, Jang, Sungjun, Kim, Minjung, Lee, Sangyoun
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background distractors that appear similar to the target objects. We propose three
Externí odkaz:
http://arxiv.org/abs/2207.06953