1 code implementation • • Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
Then, we propose the audio adapter to adapt audio representation into an audio token enriched with specific semantics, which can be injected into a frozen T2I model flexibly.
We propose to solve the task through the collaboration between Large Language Models (LLMs) and Diffusion Models: Instruct GPT-3 (davinci-002) with Chain-of-Thought prompting generates text that represents a visual elaboration of the linguistic metaphor containing the implicit meaning and relevant objects, which is then used as input to the diffusion-based text-to-image models. Using a human-AI collaboration framework, where humans interact both with the LLM and the top-performing diffusion model, we create a high-quality dataset containing 6, 476 visual metaphors for 1, 540 linguistic metaphors and their associated visual elaborations.
no code implementations • 3 May 2023 • Tao Chen, Liang Lv, Di Wang, Jing Zhang, Yue Yang, Zeyang Zhao, Chen Wang, Xiaowei Guo, Hao Chen, Qingye Wang, Yufei Xu, Qiming Zhang, Bo Du, Liangpei Zhang, DaCheng Tao
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.
Current transformer-based approaches tackle this challenging problem in a two-stage manner, which detect corners in the first model and classify the proposed edges (corner-pairs) in the second model.
The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models.
By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1.
Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining high performance in high-level recognition tasks such as classification and detection.
Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to learn the skill via reinforcement learning (RL).
Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.
In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE.
We propose to extract these properties from images and use them in an ensemble model, in order to complement the information that is extracted from language models.
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.
Ranked #2 on Visual Commonsense Tests on ViComTe-color
Data has now become a shortcoming of deep learning.
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
Schemata are structured representations of complex tasks that can aid artificial intelligence by allowing models to break down complex tasks into intermediate steps.
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
The authors of the article have reviewed the scientific literature on the development of the Russian-Chinese cooperation in the field of combining economic and logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt.
Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities.
Ranked #1 on VGSI on wikiHow-image
no code implementations • 9 Apr 2021 • Garrett A. Stevenson, Derek Jones, Hyojin Kim, W. F. Drew Bennett, Brian J. Bennion, Monica Borucki, Feliza Bourguet, Aidan Epstein, Magdalena Franco, Brooke Harmon, Stewart He, Max P. Katz, Daniel Kirshner, Victoria Lao, Edmond Y. Lau, Jacky Lo, Kevin McLoughlin, Richard Mosesso, Deepa K. Murugesh, Oscar A. Negrete, Edwin A. Saada, Brent Segelke, Maxwell Stefan, Marisa W. Torres, Dina Weilhammer, Sergio Wong, Yue Yang, Adam Zemla, Xiaohua Zhang, Fangqiang Zhu, Felice C. Lightstone, Jonathan E. Allen
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods.
However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used.
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train.
Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents.
In high-dimensions, the prior tails can have a significant effect on both posterior computation and asymptotic concentration rates.
There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other.
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
no code implementations • 30 Mar 2020 • Xiyi Wei, Yu-Tian Xiao, Jian Wang, Rui Chen, Wei zhang, Yue Yang, Daojun Lv, Chao Qin, Di Gu, Bo Zhang, Weidong Chen, Jianquan Hou, Ninghong Song, Guohua Zeng, Shancheng Ren
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences.
With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode.
This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit risk.
Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated.
We overcome the second challenge by developing a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired finer level of spatial aggregation.
This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
Then, the offset surface of the inner surface is obtained through contouring the distance field of the inner surface, and segmented to generate the outer surface.