no code implementations • 18 Jan 2024 • Fan Shi, Bin Li, xiangyang xue
In the odd-one-out task and two held-out configurations, RAISE can leverage acquired latent concepts and atomic rules to find the rule-breaking image in a matrix and handle problems with unseen combinations of rules and attributes.
no code implementations • 27 Dec 2023 • Fan Shi
In this research, we introduce RefineNet, a novel architecture designed to address resolution limitations in text-to-image conversion systems.
1 code implementation • 15 Jul 2023 • Fan Shi, Bin Li, xiangyang xue
Finally, we conduct experiments to illustrate the interpretability of CRAB in concept learning, answer selection, and global rule abstraction.
no code implementations • 7 Apr 2023 • Yifan Yin, Xu Cheng, Fan Shi, Xiufeng Liu, Huan Huo, ShengYong Chen
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity.
1 code implementation • 15 Sep 2022 • Fan Shi, Bin Li, xiangyang xue
The automatic parsing of these laws indicates the model's ability to understand the scene, which makes law parsing play a central role in many visual tasks.
no code implementations • 26 Oct 2021 • Tong Shen, Jiawei Zuo, Fan Shi, Jin Zhang, Liqin Jiang, Meng Chen, Zhengchen Zhang, Wei zhang, Xiaodong He, Tao Mei
We demonstrate ViDA-MAN, a digital-human agent for multi-modal interaction, which offers realtime audio-visual responses to instant speech inquiries.
2 code implementations • 22 Mar 2021 • Fan Shi, Bin Li, xiangyang xue
In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables.