no code implementations • 3 Jun 2022 • Ruiming Cao, Fanglin Linda Liu, Li-Hao Yeh, Laura Waller
We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution.
no code implementations • 5 Oct 2019 • Ruiming Cao, Xinran Zhong, Fabien Scalzo, Steven Raman, Kyung hyun Sung
Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning.
no code implementations • 18 Dec 2018 • Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
The AOG associates each object part with certain neural units in feature maps of conv-layers.
no code implementations • 18 Dec 2018 • Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
no code implementations • 5 Aug 2017 • Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Redmonds, Ying Nian Wu, Song-Chun Zhu
In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals.
no code implementations • 5 Aug 2017 • Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu
Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.
no code implementations • CVPR 2017 • Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows.
no code implementations • 14 Nov 2016 • Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding.