Zobrazeno 1 - 10
of 1 067
pro vyhledávání: '"GUO Ziyu"'
Effects of pre-freezing and sucrose impregnation methods on the quality of freeze-dried apple slices
Publikováno v:
Shipin yu jixie, Vol 40, Iss 6, Pp 185-191 (2024)
[Objective] This study aimed to investigate the effects of two pre-freezing methods (vacuum freezing and atmospheric pressure freezing) and two dipping methods (sucrose impregnation before pre-freezing and sucrose impregnation during freeze-drying) o
Externí odkaz:
https://doaj.org/article/00b5e0db47394f94a151eb2afedc026e
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to ho
Externí odkaz:
http://arxiv.org/abs/2411.18101
Autor:
Wang, Yi, Wang, Jiaze, Guo, Ziyu, Zhang, Renrui, Zhou, Donghao, Chen, Guangyong, Liu, Anfeng, Heng, Pheng-Ann
Recently Transformer-based models have advanced point cloud understanding by leveraging self-attention mechanisms, however, these methods often overlook latent information in less prominent regions, leading to increased sensitivity to perturbations a
Externí odkaz:
http://arxiv.org/abs/2411.14744
Diffusion Transformers (DiT) have emerged as powerful generative models for various tasks, including image, video, and speech synthesis. However, their inference process remains computationally expensive due to the repeated evaluation of resource-int
Externí odkaz:
http://arxiv.org/abs/2411.10510
Autor:
Li, Linyuan, Qiu, Jianing, Saha, Anujit, Li, Lin, Li, Poyuan, He, Mengxian, Guo, Ziyu, Yuan, Wu
As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies,
Externí odkaz:
http://arxiv.org/abs/2411.07619
Autor:
Jiang, Dongzhi, Zhang, Renrui, Guo, Ziyu, Wu, Yanmin, Lei, Jiayi, Qiu, Pengshuo, Lu, Pan, Chen, Zehui, Fu, Chaoyou, Song, Guanglu, Gao, Peng, Liu, Yu, Li, Chunyuan, Li, Hongsheng
The advent of Large Language Models (LLMs) has paved the way for AI search engines, e.g., SearchGPT, showcasing a new paradigm in human-internet interaction. However, most current AI search engines are limited to text-only settings, neglecting the mu
Externí odkaz:
http://arxiv.org/abs/2409.12959
Autor:
Guo, Ziyu, Zhang, Renrui, Zhu, Xiangyang, Tong, Chengzhuo, Gao, Peng, Li, Chunyuan, Heng, Pheng-Ann
We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point interprets any 3D data as a series of multi-directional videos, and leverages SAM 2 for 3D-space segme
Externí odkaz:
http://arxiv.org/abs/2408.16768
Autor:
Zhang, Renrui, Wei, Xinyu, Jiang, Dongzhi, Guo, Ziyu, Li, Shicheng, Zhang, Yichi, Tong, Chengzhuo, Liu, Jiaming, Zhou, Aojun, Wei, Bin, Zhang, Shanghang, Gao, Peng, Li, Chunyuan, Li, Hongsheng
The mathematical capabilities of Multi-modal Large Language Models (MLLMs) remain under-explored with three areas to be improved: visual encoding of math diagrams, diagram-language alignment, and chain-of-thought (CoT) reasoning. This draws forth an
Externí odkaz:
http://arxiv.org/abs/2407.08739
Autor:
Wang, Jiaze, Wang, Yi, Guo, Ziyu, Zhang, Renrui, Zhou, Donghao, Chen, Guangyong, Liu, Anfeng, Heng, Pheng-Ann
We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversity and improving alignment acr
Externí odkaz:
http://arxiv.org/abs/2405.18523
Autor:
Zhu, Xiangyang, Zhang, Renrui, He, Bowei, Guo, Ziyu, Liu, Jiaming, Xiao, Han, Fu, Chaoyou, Dong, Hao, Gao, Peng
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'seen' classes, and then evaluate their generalization performance on 'uns
Externí odkaz:
http://arxiv.org/abs/2404.04050