Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Zhu, Lianghui"'
Autor:
Ouyang, Mingxi, Fu, Yuqiu, Yan, Renao, Shi, ShanShan, Ling, Xitong, Zhu, Lianghui, He, Yonghong, Guan, Tian
Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of WSIs and the scarcity of manual annotations present substantial challe
Externí odkaz:
http://arxiv.org/abs/2408.12825
Autor:
Wang, Xuenian, Shi, Shanshan, Yan, Renao, Sun, Qiehe, Zhu, Lianghui, Guan, Tian, He, Yonghong
In the field of whole slide image (WSI) classification, multiple instance learning (MIL) serves as a promising approach, commonly decoupled into feature extraction and aggregation. In this paradigm, our observation reveals that discriminative embeddi
Externí odkaz:
http://arxiv.org/abs/2406.00672
Autor:
Zhu, Lianghui, Huang, Zilong, Liao, Bencheng, Liew, Jun Hao, Yan, Hanshu, Feng, Jiashi, Wang, Xinggang
Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT). However, DiT models have faced challenges with scalability and quadr
Externí odkaz:
http://arxiv.org/abs/2405.18428
Recently, linear complexity sequence modeling networks have achieved modeling capabilities similar to Vision Transformers on a variety of computer vision tasks, while using fewer FLOPs and less memory. However, their advantage in terms of actual runt
Externí odkaz:
http://arxiv.org/abs/2405.18425
Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduce
Externí odkaz:
http://arxiv.org/abs/2402.14812
Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an
Externí odkaz:
http://arxiv.org/abs/2401.09417
Evaluating Large Language Models (LLMs) in open-ended scenarios is challenging because existing benchmarks and metrics can not measure them comprehensively. To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluat
Externí odkaz:
http://arxiv.org/abs/2310.17631
This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical importance for understanding a classification network and launching WSSS. We ob
Externí odkaz:
http://arxiv.org/abs/2304.01184
Autor:
Zheng, Runliang, Wang, Xuenian, Zhu, Lianghui, Yan, Renao, Li, Jiawen, Wei, Yani, Zhang, Fenfen, Du, Hong, Guo, Linlang, He, Yonghong, Shi, Huijuan, Han, Anjia
Publikováno v:
In iScience 20 September 2024 27(9)
Publikováno v:
In Environmental Pollution 1 June 2024 350