Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.
Weakly Supervised Semantic Segmentation (WSSS), which relies only on image-level labels, has attracted significant attention for its cost-effectiveness and scalability. Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations. However, they often neglect the complex contextual dependencies among image patch...
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing high-resolution volumetric data. However, these approaches often neglect the patch's location within the global volume, which can limit segmentation performance when anatomical context is important. In this paper, we investigate the role of location context in patch-based 3D segmentatio...
Suboptimal color representation often hinders accurate image segmentation, yet many modern algorithms neglect this critical preprocessing step. This w...
Unauthorized screen-shooting poses a critical data leakage risk. Resisting screen-shooting attacks typically requires high-strength watermark embeddin...
The growth of generative AI and easily available Open Access health datasets has transformed researcher productivity, leading to an explosion in publi...
Understanding how pedoclimatic drivers regulate soil organic carbon (SOC) stock is crucial for gaining insights into terrestrial carbon-climate feedba...
Deep hashing is an effective approach for large-scale image retrieval. Current methods are typically classified by their supervision types: point-wi...
Quantifying uncertainty in neural implicit 3D representations, particularly those utilizing Signed Distance Functions (SDFs), remains a substantial ...
Bad actors, primarily distressed firms, have the incentive and desire to manipulate their financial reports to hide their distress and derive person...
Although the lightweight Vision Transformer has significantly advanced image super-resolution (SR), it faces the inherent challenge of a limited rec...
This work focuses on modeling dynamic urban environments for autonomous driving simulation. Contemporary data-driven methods using neural radiance f...
A Wi-Fi-enabled device, or simply Wi-Fi device, sporadically broadcasts probe request frames (PRFs) to discover nearby access points (APs), whether ...
High-definition (HD) map construction methods are crucial for providing precise and comprehensive static environmental information, which is essenti...
MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity o...
Text-to-image diffusion models often struggle to achieve accurate semantic alignment between generated images and text prompts while maintaining eff...
As the core operator of Transformers, Softmax Attention exhibits excellent global modeling capabilities. However, its quadratic complexity limits it...
Artificial intelligence (AI), particularly deep learning, has demonstrated high diagnostic performance in detecting intracranial aneurysms on computed...
Knowledge Tracing (KT), as a pivotal technology in intelligent education systems, analyzes students' learning data to infer their knowledge acquisitio...
We introduce DABstep, a novel benchmark for evaluating AI agents on realistic multi-step data analysis tasks. DABstep comprises over 450 real-world ...
Since there exists a notable gap between user-provided and model-preferred prompts, generating high-quality and satisfactory images using diffusion ...