Latest AI and machine learning research in transplantation for healthcare professionals.
This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature of event cameras in capturing only the outlines of motion, combined with SNNs' proficiency in processing spatiotemporal data through spikes, establishes a highly synergistic compatibility for event-based HAR. Previous...
Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing images more challenging. Moreover, existing semantic segmentation models for remote sensing images usually employ a vanilla softmax classifier, which has three drawbacks: (1) non-direct supervision for the pixel represen...
Achieving flexible and high-fidelity identity-preserved image generation remains formidable, particularly with advanced Diffusion Transformers (DiTs...
Precise audio-visual synchronization in speech videos is crucial for content quality and viewer comprehension. Existing methods have made significan...
We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG)...
This paper investigates the prospects of AI without representation in general, and the proposals of Rodney Brooks in particular. What turns out to b...
Accurate segmentation is essential for effective treatment planning and disease monitoring. Existing medical image segmentation methods predominantl...
Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, wh...
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting th...
Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In th...
Text-to-image synthesis has witnessed remarkable advancements in recent years. Many attempts have been made to adopt text-to-image models to support...
This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an i...
Background: Automated analysis of CT scans for abdominal organ measurement is crucial for improving diagnostic efficiency and reducing inter-observe...
Solving medical imaging data scarcity through semantic image generation has attracted significant attention in recent years. However, existing metho...
Bag-based Multiple Instance Learning (MIL) approaches have emerged as the mainstream methodology for Whole Slide Image (WSI) classification. However...
Human-centric visual perception (HVP) has recently achieved remarkable progress due to advancements in large-scale self-supervised pretraining (SSP)...
Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synth...
Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial sp...
Recent advancements in Unet-based diffusion models, such as ControlNet and IP-Adapter, have introduced effective spatial and subject control mechani...
This paper focuses on multimodal alignment within the realm of Artificial Intelligence, particularly in text and image modalities. The semantic gap ...