no code implementations • 24 Jun 2017 • Tianyi Zhao, Jun Yu, Zhenzhong Kuang, Wei zhang, Jianping Fan
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.
no code implementations • 8 Jul 2017 • Tianyi Zhao, Baopeng Zhang, Wei zhang, Ning Zhou, Jun Yu, Jianping Fan
Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.
no code implementations • CVPR 2021 • Tianyi Zhao, Kai Cao, Jiawen Yao, Isabella Nogues, Le Lu, Lingyun Huang, Jing Xiao, Zhaozheng Yin, Ling Zhang
We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging.
no code implementations • 25 Mar 2023 • Tianyi Zhao, Shamik Sarkar, Enes Krijestorac, Danijela Cabric
We also propose two deep-learning approaches (SD-RXA and GAN-RXA) in this first stage to improve the receiver-agnostic property of the RXA framework.
1 code implementation • 28 Jun 2023 • Maoxun Yuan, Tianyi Zhao, Bo Li, Xingxing Wei
To address this issue, in this paper we observe that the spatial details from PAN images are mainly high-frequency cues, i. e., the edges reflect the contour of input PAN images.
no code implementations • 25 Aug 2023 • Tianyi Zhao, Hui Hu, Lu Cheng
Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks.
no code implementations • 19 Jan 2024 • Tianyi Zhao, Maoxun Yuan, Xingxing Wei
Specifically, following this perspective, we design a Redundant Spectrum Removal module to coarsely remove interfering information within each modality and a Dynamic Feature Selection module to finely select the desired features for feature fusion.
no code implementations • 8 Feb 2024 • Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs.