Latest AI and machine learning research in covid-19 for healthcare professionals.
The continuous improvements on image compression with variational autoencoders have lead to learned codecs competitive with conventional approaches in terms of rate-distortion efficiency. Nonetheless, taking the quantization into account during the training process remains a problem, since it produces zero derivatives almost everywhere and needs to be replaced with a differentiable approximation...
Rapid advancements in RISC-V hardware development shift the focus from low-level optimizations to higher-level parallelization. Recent RISC-V processors, such as the SOPHON SG2042, have 64 cores. RISC-V processors with core counts comparable to the SG2042, make efficient parallelization as crucial for RISC-V as the more established processors such as x86-64. In this work, we evaluate the paralle...
Several variants of Neural Radiance Fields (NeRFs) have significantly improved the accuracy of synthesized images and surface reconstruction of 3D s...
Private Evolution (PE) is a promising training-free method for differentially private (DP) synthetic data generation. While it achieves strong perfo...
How can we generate an image B' that satisfies A:A'::B:B', given the input images A,A' and B? Recent works have tackled this challenge through appro...
Accurately perceiving complex driving environments is essential for ensuring the safe operation of autonomous vehicles. With the tremendous progress...
Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable gr...
The goal of the correspondence task is to segment specific objects across different views. This technical report re-defines cross-image segmentation...
The COVID-19 pandemic's severe impact highlighted the need for accurate, timely hospitalization forecasting to support effective healthcare planning...
Large language models (LLMs) achieve impressive performance on various knowledge-intensive and complex reasoning tasks in different domains. In cert...
Recent image segmentation models have advanced to segment images into high-quality masks for visual entities, and yet they cannot provide comprehens...
Spatio-temporal localization is vital for precise interactions across diverse domains, from biological research to autonomous navigation and interac...
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearab...
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Contrastive self-su...
Brain-Computer Interfaces (BCIs) based on motor imagery (MI) hold promise for restoring control in individuals with motor impairments. However, up t...
Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fai...
We present LayerFlow, a unified solution for layer-aware video generation. Given per-layer prompts, LayerFlow generates videos for the transparent f...
Large language models (LLMs) are known to be sensitive to input phrasing, but the mechanisms by which semantic cues shape reasoning remain poorly un...
3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing met...
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzi...