no code implementations • 13 Oct 2023 • Qian Chen, Zilong Wang, Jiaqi Hu, Haonan Yan, Jianying Zhou, Xiaodong Lin
Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server).
1 code implementation • 6 Jun 2023 • Fobo Shi, Peijun Qing, Dong Yang, Nan Wang, Youbo Lei, Haonan Lu, Xiaodong Lin, Duantengchuan Li
To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space.
no code implementations • 25 May 2023 • Yiqi Lin, Hao Wu, Ruichen Wang, Haonan Lu, Xiaodong Lin, Hui Xiong, Lin Wang
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.
1 code implementation • 23 May 2023 • Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, Xiaodong Lin
Our approach produces a more semantically accurate synthesis by constraining the attention regions of each token in the prompt to the image.
1 code implementation • 27 Apr 2023 • Defeng Xie, Ruichen Wang, Jian Ma, Chen Chen, Haonan Lu, Dong Yang, Fobo Shi, Xiaodong Lin
We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs.
1 code implementation • CVPR 2023 • Ziyao Guo, Haonan Yan, Hui Li, Xiaodong Lin
Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network.
3 code implementations • 31 Mar 2023 • Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, Xiaodong Lin
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters.
Optical Character Recognition (OCR) Text-to-Image Generation
no code implementations • 24 Mar 2023 • Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang, Sijia Li, Haonan Lu, Xiaodong Lin, Lin Wang
To tackle the issue of 'guidance collapse' and enhance consistency, we propose a novel framework, dubbed CompoNeRF, by integrating an editable 3D scene layout with object specific and scene-wide guidance mechanisms.
no code implementations • 20 Nov 2022 • Tianyi Wang, Xin Liao, Kam Pui Chow, Xiaodong Lin, Yinglong Wang
In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective.
1 code implementation • 27 Oct 2022 • Dong Yang, Peijun Qing, Yang Li, Haonan Lu, Xiaodong Lin
However, it remains challenging to model the negation and union operator.
1 code implementation • 23 Dec 2021 • Denghui Zhang, Zixuan Yuan, Hao liu, Xiaodong Lin, Hui Xiong
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.
1 code implementation • 11 Aug 2020 • Haonan Lu, Hailin Hu, Xiaodong Lin
This design principle leads to several advantages of our method: (1) For composite relations, the corresponding diagonal relation matrices can be non-commutative, reflecting a predominant scenario in real world applications; (2) Our model preserves the natural interaction between relational operations and entity embeddings; (3) The scaling operation provides the modeling power for the intrinsic semantic hierarchical structure of entities; (4) The enhanced expressiveness of DensE is achieved with high computational efficiency in terms of both parameter size and training time; and (5) Modeling entities in Euclidean space instead of quaternion space keeps the direct geometrical interpretations of relational patterns.
Ranked #7 on Link Prediction on WN18