Latest AI and machine learning research in identifying and reporting child abuse for healthcare professionals.
Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The core challenge lies in minimizing the commonalities among different sources. Existing deep learning approaches either neglect the significance of feature interactions or rely on heuristically de...
Soybean leaf disease detection is critical for agricultural productivity but faces challenges due to visually similar symptoms and limited interpretability in conventional methods. While Convolutional Neural Networks (CNNs) excel in spatial feature extraction, they often neglect inter-image relational dependencies, leading to misclassifications. This paper proposes an interpretable hybrid Sequen...
Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such a...
Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendati...
The accurate assessment of the brain's functional network is seen as crucial for the understanding of complex relationships between different brain re...
Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based c...
Recent studies have shown that Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors, resulting i...
Medical image segmentation plays a crucial role in various clinical applications. A major challenge in medical image segmentation is achieving accur...
It is well known that query-based attacks tend to have relatively higher success rates in adversarial black-box attacks. While research on black-box...
White blood cells (WBC) are important parts of our immune system, and they protect our body against infections by eliminating viruses, bacteria, par...
Map construction task plays a vital role in providing precise and comprehensive static environmental information essential for autonomous driving sy...
Attention-based arbitrary style transfer methods have gained significant attention recently due to their impressive ability to synthesize style deta...
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness reg...
Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In r...
Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization ca...
Automated segmentation plays a pivotal role in medical image analysis and computer-assisted interventions. Despite the promising performance of exis...
Artificial intelligence (AI) systems in healthcare have demonstrated remarkable potential to improve patient outcomes. However, if not designed with...
In this study, we reveal that the interaction between haze degradation and JPEG compression introduces complex joint loss effects, which significant...
Given multi-type point maps from different place-types (e.g., tumor regions), our objective is to develop a classifier trained on the source place-t...
Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge...