Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Xiang, Suncheng"'
Parameter-efficient tuning (PET) techniques calibrate the model's predictions on downstream tasks by freezing the pre-trained models and introducing a small number of learnable parameters. However, despite the numerous PET methods proposed, their rob
Externí odkaz:
http://arxiv.org/abs/2410.09845
Objective: Depth estimation is crucial for endoscopic navigation and manipulation, but obtaining ground-truth depth maps in real clinical scenarios, such as the colon, is challenging. This study aims to develop a robust framework that generalizes wel
Externí odkaz:
http://arxiv.org/abs/2409.15006
Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras, which plays an important role in the prevention and treatment of colorectal cancer in computer-aide
Externí odkaz:
http://arxiv.org/abs/2408.05914
Autor:
Ruan, Jiacheng, Gao, Jingsheng, Xie, Mingye, Dong, Daize, Xiang, Suncheng, Liu, Ting, Fu, Yuzhuo
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks. This paper proposes a distillation fr
Externí odkaz:
http://arxiv.org/abs/2403.15750
In the realm of medical image segmentation, both CNN-based and Transformer-based models have been extensively explored. However, CNNs exhibit limitations in long-range modeling capabilities, whereas Transformers are hampered by their quadratic comput
Externí odkaz:
http://arxiv.org/abs/2402.02491
Autor:
Li, Zeyu, Xiang, Suncheng, Yu, Tong, Gao, Jingsheng, Ruan, Jiacheng, Hu, Yanping, Liu, Ting, Fu, Yuzhuo
The recognition of underwater audio plays a significant role in identifying a vessel while it is in motion. Underwater target recognition tasks have a wide range of applications in areas such as marine environmental protection, detection of ship radi
Externí odkaz:
http://arxiv.org/abs/2401.02099
Recently, Visual Transformer (ViT) has been extensively used in medical image segmentation (MIS) due to applying self-attention mechanism in the spatial domain to modeling global knowledge. However, many studies have focused on improving models in th
Externí odkaz:
http://arxiv.org/abs/2312.17030
Autor:
Gao, Jingsheng, Ruan, Jiacheng, Xiang, Suncheng, Yu, Zefang, Ji, Ke, Xie, Mingye, Liu, Ting, Fu, Yuzhuo
With the success of pre-trained visual-language (VL) models such as CLIP in visual representation tasks, transferring pre-trained models to downstream tasks has become a crucial paradigm. Recently, the prompt tuning paradigm, which draws inspiration
Externí odkaz:
http://arxiv.org/abs/2312.08212
Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-c
Externí odkaz:
http://arxiv.org/abs/2312.07623
Autor:
Ruan, Jiacheng, Gao, Jingsheng, Xie, Mingye, Xiang, Suncheng, Yu, Zefang, Liu, Ting, Fu, Yuzhuo
The Parameter-Efficient Fine-Tuning (PEFT) method, which adjusts or introduces fewer trainable parameters to calibrate pre-trained models on downstream tasks, has become a recent research interest. However, existing PEFT methods within the traditiona
Externí odkaz:
http://arxiv.org/abs/2312.07255