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Found 44 papers, 18 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
Text-to-image (T2I) generative models have attracted significant attention and found extensive applications within and beyond academic research.
CoMo: Controllable Motion Generation through Language Guided Pose Code Editing
Text-to-motion models excel at efficient human motion generation, but existing approaches lack fine-grained controllability over the generation process.
Augmented Reality Demonstrations for Scalable Robot Imitation Learning
Our framework facilitates scalable and diverse demonstration collection for real-world tasks.
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification.
Towards Implicit Prompt For Text-To-Image Models
We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.
Annotated Hands for Generative Models
These annotations provide additional structure that coax the generative model to produce higher quality hand images.
Holodeck: Language Guided Generation of 3D Embodied AI Environments
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope.
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability.
Align, Adapt and Inject: Sound-guided Unified Image Generation
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.
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors
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.
Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey
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.
Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving.
CornerFormer: Boosting Corner Representation for Fine-Grained Structured Reconstruction
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.
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module
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.
Causal Reasoning of Entities and Events in Procedural Texts
By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1.
Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer
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.
Safe Inverse Reinforcement Learning via Control Barrier Function
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).
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
In this paper, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE.
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction
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.
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks.
Ranked #2 on
Visual Commonsense Tests
on ViComTe-color
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
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.
Induce, Edit, Retrieve:Language Grounded Multimodal Schema for Instructional Video Retrieval
Schemata are structured representations of complex tasks that can aid artificial intelligence by allowing models to break down complex tasks into intermediate steps.
Foreground Object Structure Transfer for Unsupervised Domain Adaptation
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.
Economic prospects of the Russian-Chinese partnership in the logistics projects of the Eurasian Economic Union and the Silk Road Economic Belt: a scientific literature review
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.
Visual Goal-Step Inference using wikiHow
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
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods.
Deceptive Reinforcement Learning for Privacy-Preserving Planning
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.
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train.
Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island
Nowadays, autonomous taxis become a highly promising transportation mode, which helps relieve traffic congestion and avoid road accidents.
Variational approximations of empirical Bayes posteriors in high-dimensional linear models
In high-dimensions, the prior tails can have a significant effect on both posterior computation and asymptotic concentration rates.
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms
There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other.
Microwave Photonic Imaging Radar with a Millimeter-level Resolution
Microwave photonic radars enable fast or even real-time high-resolution imaging thanks to its broad bandwidth.
MedDialog: Two Large-scale Medical Dialogue Datasets
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.
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
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.
How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing
With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode.
Optimal Dividend Strategy for an Insurance Group with Contagious Default Risk
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.
A hybrid model based on CNN and Bi-LSTM for urban water demand prediction
The training time of CNN-Bi-LSTM is less than LSTM, Bi-LSTM, CNN, and CNN-LSTM, but larger than SAEs.
Variational approximations using Fisher divergence
Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated.
A causal inference framework for cancer cluster investigations using publicly available data
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.
Methodology Applications
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles
This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
A semi-automatic computer-aided method for surgical template design
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.
