Search Results for author: Yuhan Zhu

Found 8 papers, 4 papers with code

Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments

no code implementations19 Jun 2024 Yuhan Zhu, Jian Wang, Bing Li, Xuxian Tang, Hao Li, Neng Zhang, Yuqi Zhao

Experiments conducted on the dataset collected from the benchmark show that MicroCERCL can accurately localize the root cause of microservice systems in such environments, significantly outperforming state-of-the-art approaches with an increase of at least 24. 1% in top-1 accuracy.

Graph Neural Network

Dual DETRs for Multi-Label Temporal Action Detection

no code implementations CVPR 2024 Yuhan Zhu, Guozhen Zhang, Jing Tan, Gangshan Wu, LiMin Wang

To address this issue, we propose a new Dual-level query-based TAD framework, namely DualDETR, to detect actions from both instance-level and boundary-level.

Action Detection object-detection +2

ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video

2 code implementations2 Oct 2023 Xinhao Li, Yuhan Zhu, LiMin Wang

In this paper, we present a new adaptation paradigm (ZeroI2V) to transfer the image transformers to video recognition tasks (i. e., introduce zero extra cost to the original models during inference).

Ranked #6 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition +1

DPL: Decoupled Prompt Learning for Vision-Language Models

no code implementations19 Aug 2023 Chen Xu, Yuhan Zhu, Guozhen Zhang, Haocheng Shen, Yixuan Liao, Xiaoxin Chen, Gangshan Wu, LiMin Wang

Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e. g., CLIP) to downstream tasks.

Progressive Visual Prompt Learning with Contrastive Feature Re-formation

1 code implementation17 Apr 2023 Chen Xu, Yuhan Zhu, Haocheng Shen, Boheng Chen, Yixuan Liao, Xiaoxin Chen, LiMin Wang

To the best of our knowledge, we are the first to demonstrate the superior performance of visual prompts in V-L models to previous prompt-based methods in downstream tasks.

Cannot find the paper you are looking for? You can Submit a new open access paper.