898 papers
This paper proposes a lightweight YOLOv11n-OBB-based detection method featuring a Surgical Precision replacement strategy with GhostConv_OBB and C3k2_GhostConv_OBB modules, which achieves high-precision, real-time oriented detection of Luosifen outer packages on edge devices while significantly outperforming the baseline in accuracy and efficiency.
This study introduces the Explainability Agreement Index (EAI), a novel metric that quantifies the alignment between AI-generated explanations and clinician reasoning in chronic wound diagnosis, revealing that despite high classification accuracy, current AI models often lack the interpretability required for clinical trustworthiness.
The paper proposes PMDA-Net, a progressive multi-level dynamic attention network that enhances crowd counting accuracy and robustness against scale variation, occlusion, and background noise through a novel architecture featuring feature calibration, cross-scale interaction, density-aware attention, and foreground-guided optimization.
This paper presents an ultra-compact, two-stage on-device audio cascade that utilizes a lightweight 2D CNN for snore detection and an adapted Coordinate Attention mechanism for classifying sleep apnea events, achieving high accuracy with a 93.2% parameter reduction compared to baselines.
This paper proposes a systematic conceptual framework and algorithmic extensions for spatio-temporal cartographic generalization that integrate temporal characteristics to preserve meaningful change events while ensuring map legibility, demonstrated through applications in data classification and line simplification.
This paper proposes an optimized Monodepth2-based monocular vision measurement method that integrates semantic segmentation and fixed-weight spatial correction using prior geometric features to achieve sub-millimeter accuracy in construction scenarios, demonstrating over 96% error reduction compared to conventional approaches.
This paper proposes a lightweight adaptive YOLOv11 detector integrated with a closed-loop cybernetic control system to achieve high-speed, high-accuracy industrial defect perception and real-time sorting, significantly improving inference efficiency and control stability compared to conventional methods.
This paper presents a reproducible benchmark demonstrating that while off-the-shelf self-supervised vision embeddings like DINOv2 can effectively generate leads for surface re-identification in OSINT, they remain insufficient for unqualified identification due to significant performance degradation across scale variations and on smooth surfaces.
This study challenges the assumption of language-invariant behavior in multilingual LLMs by demonstrating through a controlled empirical analysis that semantic, sentiment, and safety-related responses vary significantly across English, Hindi, and French, revealing that behavioral divergence is real and category-dependent rather than strictly following linguistic-distance predictions.
This quasi-experimental study demonstrates that AI-assisted non-programmers produced code with higher maintainability and structural quality scores than programmers working without AI, with the quality of human–AI interaction emerging as a strong predictor of these outcomes.