Latest AI and machine learning research in identifying and reporting child abuse for healthcare professionals.
Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike traditional Class-Incremental Learning (CIL), VCIL introduces the added complexity of spatiotemporal structures, making it particularly challenging to mitigate catastrophic forgetting while effectively capturing both fram...
Despite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minority learning tasks, showing how standard training can favor majority groups and produce stereotypical predictors that neglect minority-specific features. Assuming population and variance imbalance, our analysis reveals...
Human preference plays a crucial role in the refinement of large language models (LLMs). However, collecting human preference feedback is costly and...
Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably dete...
Diffusion transformers have shown exceptional performance in visual generation but incur high computational costs. Token reduction techniques that c...
Diffusion models have made substantial advances in image generation, yet models trained on large, unfiltered datasets often yield outputs misaligned...
Advances in artificial intelligence (AI) and intelligent computing have significantly accelerated drug discovery by enabling accurate modeling of comp...
The design and implementation of unit tests is a complex task many programmers neglect. This research evaluates the potential of Large Language Mode...
State-of-the-art LiDAR-camera 3D object detectors usually focus on feature fusion. However, they neglect the factor of depth while designing the fus...
Various multi-instance learning (MIL) based approaches have been developed and successfully applied to whole-slide pathological images (WSI). Existi...
BACKGROUND: Understanding protein-molecular interaction is crucial for unraveling the mechanisms underlying diverse biological processes. Machine lear...
Existing deep learning-based image inpainting methods typically rely on convolutional networks with RGB images to reconstruct images. However, relyi...
The Mamba model has recently demonstrated strong potential in hyperspectral image (HSI) classification, owing to its ability to perform context mode...
Electrical Impedance Tomography (EIT) provides a non-invasive, portable imaging modality with significant potential in medical and industrial applic...
Safety assessment of patients with one-dimensionally structured passive implants, like cranial plates or stents, exposed to low or medium frequency ...
Birth weight (BW) is a key indicator of neonatal health, with low birth weight (LBW) linked to increased mortality and morbidity. Early prediction o...
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challen...
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challen...
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abund...
Enhancing forward-looking sonar images is critical for accurate underwater target detection. Current deep learning methods mainly rely on supervised...