Latest AI and machine learning research in covid-19 for healthcare professionals.
Segment Anything Models (SAMs), as vision foundation models, have demonstrated remarkable performance across various image analysis tasks. Despite their strong generalization capabilities, SAMs encounter challenges in fine-grained detail segmentation for high-resolution class-independent segmentation (HRCS), due to the limitations in the direct processing of high-resolution inputs and low-resolu...
Referring Video Object Segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This task has attracted increasing attention in the field of computer vision due to its promising applications in video editing and human-agent interaction. Recently, ReferDINO has demonstrated promising performance in this task by adapting object-level vision-language knowle...
Electronic Health Records (EHR) have become a valuable resource for a wide range of predictive tasks in healthcare. However, existing approaches hav...
MOTIVATION: Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibo...
With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive geneti...
Zero-shot depth estimation (DE) models exhibit strong generalization performance as they are trained on large-scale datasets. However, existing mode...
We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery ima...
In this paper, we introduce zero-shot audio-video editing, a novel task that requires transforming original audio-visual content to align with a spe...
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and ...
The level set estimation problem seeks to identify regions within a set of candidate points where an unknown and costly to evaluate function's value...
Sora has unveiled the immense potential of the Diffusion Transformer (DiT) architecture in single-scene video generation. However, the more challeng...
Segmentation is a fundamental task in computer vision, with prompt-driven methods gaining prominence due to their flexibility. The Segment Anything ...
The acquisition of annotated datasets with paired images and segmentation masks is a critical challenge in domains such as medical imaging, remote s...
We present a target-aware video diffusion model that generates videos from an input image in which an actor interacts with a specified target while ...
High-quality test datasets are crucial for assessing the reliability of Deep Neural Networks (DNNs). Mutation testing evaluates test dataset quality...
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challe...
Recent advances in diffusion models bring new vitality to visual content creation. However, current text-to-video generation models still face signi...
High-resolution semantic segmentation is essential for applications such as image editing, bokeh imaging, AR/VR, etc. Unfortunately, existing datase...
Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. Existing methods ...
Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applica...