Latest AI and machine learning research in covid-19 for healthcare professionals.
Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is challenging to gather. We present FFa...
The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot classification and retrieval capabilities on various domains. However, CLIP's training remains computationally intensive, with high demands on both data processing and memory. To address these challenges, recent maskin...
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of de...
This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa)....
While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earr...
Traditional transformer-based semantic segmentation relies on quantized embeddings. However, our analysis reveals that autoencoder accuracy on segme...
The application of large language models (LLMs) to healthcare information extraction has emerged as a promising approach. This study evaluates the c...
Video object segmentation is crucial for the efficient analysis of complex medical video data, yet it faces significant challenges in data availabil...
This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose...
Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular ...
Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understanding, i.e., they endow large language models with v...
NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the priv...
Rectified flow models have achieved remarkable performance in image and video generation tasks. However, existing numerical solvers face a trade-off...
Reconstructing clean, distractor-free 3D scenes from real-world captures remains a significant challenge, particularly in highly dynamic and clutter...
The acquisition of large-scale and diverse demonstration data are essential for improving robotic imitation learning generalization. However, genera...
The remarkable performance of large multimodal models (LMMs) has attracted significant interest from the image segmentation community. To align with...
Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To...
This paper introduces TuneNSearch, a hybrid transfer learning and local search approach for addressing different variants of vehicle routing problem...
Existing image-based virtual try-on methods directly transfer specific clothing to a human image without utilizing clothing attributes to refine the...
We propose the notion of empirical privacy variance and study it in the context of differentially private fine-tuning of language models. Specifical...