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Found 8 papers, 2 papers with code
Structure-Guided Adversarial Training of Diffusion Models
In this pioneering approach, we compel the model to learn manifold structures between samples in each training batch.
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes.
Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving
Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy.
Improving Diffusion-Based Image Synthesis with Context Prediction
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Ranked #1 on
Image Inpainting
on CelebA
(LPIPS metric)
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay.
Diffusion Models: A Comprehensive Survey of Methods and Applications
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
A view synthesis-based 360° VR caching system over MEC-Enabled C-RAN
If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server.
R3: A Reading Comprehension Benchmark Requiring Reasoning Processes
Existing question answering systems can only predict answers without explicit reasoning processes, which hinder their explainability and make us overestimate their ability of understanding and reasoning over natural language.
