A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Zhengxia Zou, Keyan Chen, Zhenwei Shi, et al.
A comprehensive survey tracing 25 years of object detection evolution, from 1990s hand-crafted methods to modern deep learning detectors, covering milestones, datasets, metrics, and architectures.
Shaoqing Ren, Kaiming He, Ross Girshick, et al.
Introduces a Region Proposal Network (RPN) that shares convolutional features with Fast R-CNN, enabling nearly cost-free region proposals and real-time object detection.
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This paper formalizes when benchmark contamination is statistically detectable, distinguishing clean benchmarks from low-power audits and proposing power-calibrated detection methods.
Floriane C. M. Braun
A nuisance-controlled residual-stream probing method for detecting benchmark contamination in language models.
Watchara Ruangsang, Patiyuth Pramkeaw
Cross-dataset evaluation of YOLOv8 for UAV fire and smoke detection reveals benchmark contamination and strong zero-shot transfer.
Mark A. Richards
A comprehensive textbook covering digital signal processing techniques for modern radar systems, including matched filtering, waveform design, Doppler processing, and detection.
Fernando Cañadas-Aránega, José C. Moreno, José L. Blanco-Claraco, et al.
A low-cost monocular Visual-SLAM pipeline combining Hierarchical Localization and GLOMAP detects and 3D-reconstructs occluded tomatoes in greenhouses for robotized harvesting.
De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al.
This paper provides an end-to-end review of sensor hardware, calibration, and fusion techniques for object detection in autonomous vehicles, highlighting challenges and future research directions.
Kabila Haile Soboka
This paper introduces RibAssist 3D, a staged diagnostic study that pairs rib fractures across two orthogonal CT-derived projections to achieve selective 3D localization, identifying cross-view correspondence as the dominant bottleneck.
Ali Borji, Ming-Ming Cheng, Qibin Hou, et al.
A comprehensive survey of salient object detection covering 228 publications, including roots, core techniques, datasets, evaluation metrics, and future directions.
Amgad Muneer, Suliman Mohamed Fati
This paper compares seven machine learning classifiers for detecting cyberbullying on Twitter using a global dataset of 37,373 tweets, finding Logistic Regression achieves the best accuracy and F1 score.
Yung-Chung Wang, Yi-Chun Houng, Han-Xuan Chen, et al.
This paper evaluates six deep learning models on the CSE-CIC-IDS2018 dataset for network intrusion detection, achieving multi-class classification accuracy above 98% and comparing inference times for practical deployment.