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20212023
most citedFederated Learning for Cross-block Oil-water Layer Identification

3 citations · 6 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2023

Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models

Zengxiang Li, Zhaoxiang Hou, Hui Liu +8

Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal larg…

cs.CV2023

Predicting Token Impact Towards Efficient Vision Transformer

Hong Wang, Su Yang, Xiaoke Huang +1

Token filtering to reduce irrelevant tokens prior to self-attention is a straightforward way to enable efficient vision Transformer. This is the first work to view token filtering…

cs.CV2023

SIEDOB: Semantic Image Editing by Disentangling Object and Background

Wuyang Luo, Su Yang, Xinjian Zhang +1

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects…

cs.CV2023

Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control

Wuyang Luo, Su Yang, Weishan Zhang

Face inpainting aims at plausibly predicting missing pixels of face images within a corrupted region. Most existing methods rely on generative models learning a face image distribu…

cs.CV2022

Context-Consistent Semantic Image Editing with Style-Preserved Modulation

Wuyang Luo, Su Yang, Hong Wang +2

Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region. A recent work borrows SPADE block to achieve semantic image editing.…

cs.LG20213 cited

Feature-context driven Federated Meta-Learning for Rare Disease Prediction

Bingyang Chen, Tao Chen, Xingjie Zeng +5

Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. In addition, due to the sensi…