Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Lu, Yanye"'
The primary goal of continual learning (CL) task in medical image segmentation field is to solve the "catastrophic forgetting" problem, where the model totally forgets previously learned features when it is extended to new categories (class-level) or
Externí odkaz:
http://arxiv.org/abs/2406.13583
In the realm of image quantization exemplified by VQGAN, the process encodes images into discrete tokens drawn from a codebook with a predefined size. Recent advancements, particularly with LLAMA 3, reveal that enlarging the codebook significantly en
Externí odkaz:
http://arxiv.org/abs/2406.11837
In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The foundational concept of our method views an image as a linguistic entit
Externí odkaz:
http://arxiv.org/abs/2403.07874
Scribble-based weakly-supervised semantic segmentation using sparse scribble supervision is gaining traction as it reduces annotation costs when compared to fully annotated alternatives. Existing methods primarily generate pseudo-labels by diffusing
Externí odkaz:
http://arxiv.org/abs/2402.17555
Autor:
Zeng, Shuang, Zhu, Lei, Zhang, Xinliang, Chen, Qian, He, Hangzhou, Jin, Lujia, Tian, Zifeng, Ren, Qiushi, Xie, Zhaoheng, Lu, Yanye
Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learning offers a promising but still problematic solution to this dilemma.
Externí odkaz:
http://arxiv.org/abs/2309.11876
End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation model in a single-stage training process based on only image annotations. Existing methods adopt an online-trained classification branch to provide pseudo annotation
Externí odkaz:
http://arxiv.org/abs/2308.04949
The performance of learning-based denoising largely depends on clean supervision. However, it is difficult to obtain clean images in many scenes. On the contrary, the capture of multiple noisy frames for the same field of view is available and often
Externí odkaz:
http://arxiv.org/abs/2302.11544
Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-
Externí odkaz:
http://arxiv.org/abs/2207.07818
Weakly supervised object localization (WSOL) focuses on localizing objects only with the supervision of image-level classification masks. Most previous WSOL methods follow the classification activation map (CAM) that localizes objects based on the cl
Externí odkaz:
http://arxiv.org/abs/2203.01714
Autor:
Zhang, Xinliang, Chen, Qian, He, Hangzhou, Zhu, Lei, Xie, Zhaoheng, Lu, Yanye, Cheng, Fangxiao
Publikováno v:
In Expert Systems With Applications 1 February 2025 261
