Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Son, Jeany"'
Autor:
Yu, Seonghoon, Jung, Ilchae, Han, Byeongju, Kim, Taeoh, Kim, Yunho, Wee, Dongyoon, Son, Jeany
Referring image segmentation (RIS) requires dense vision-language interactions between visual pixels and textual words to segment objects based on a given description. However, commonly adapted dual-encoders in RIS, e.g., Swin transformer and BERT (u
Externí odkaz:
http://arxiv.org/abs/2408.15521
We propose a new framework that automatically generates high-quality segmentation masks with their referring expressions as pseudo supervisions for referring image segmentation (RIS). These pseudo supervisions allow the training of any supervised RIS
Externí odkaz:
http://arxiv.org/abs/2407.07412
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning. Existing unsupervised and self-supervised object localiza
Externí odkaz:
http://arxiv.org/abs/2309.04172
Referring image segmentation (RIS) aims to find a segmentation mask given a referring expression grounded to a region of the input image. Collecting labelled datasets for this task, however, is notoriously costly and labor-intensive. To overcome this
Externí odkaz:
http://arxiv.org/abs/2303.17811
Deep regression models typically learn in an end-to-end fashion without explicitly emphasizing a regression-aware representation. Consequently, the learned representations exhibit fragmentation and fail to capture the continuous nature of sample orde
Externí odkaz:
http://arxiv.org/abs/2210.01189
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of
Externí odkaz:
http://arxiv.org/abs/2001.11207
We present a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet). The proposed approach accelerates feature extraction procedure and learns more discriminative models for instance classification;
Externí odkaz:
http://arxiv.org/abs/1808.08834
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Deep AI military staff: cooperative battlefield situation awareness for commander's decision making.
Publikováno v:
Journal of Supercomputing; Apr2023, Vol. 79 Issue 6, p6040-6069, 30p
Publikováno v:
2015 IEEE International Conference on Computer Vision (ICCV); 1/1/2015, p3056-3064, 9p