A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Xiangxin Meng, Zexiong Ma, Pengfei Gao, et al.
This paper systematically analyzes six LLM-based agent systems for automated bug fixing on the SWE-bench Verified benchmark, comparing their overall performance, fault localization accuracy, and bug reproduction capabilities.
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.
Fabio Galbusera, Gloria Casaroli, Tito Bassani
A narrative review of AI and ML techniques applied to spine research, covering localization, segmentation, diagnosis, outcome prediction, decision support, and ethical issues.
Jingkang Yang, Kaiyang Zhou, Yixuan Li, et al.
A comprehensive survey that unifies OOD detection, OOD localization, and OOD synthesis under a generalized framework, providing a taxonomy, benchmarks, and future directions.
Jo Schlemper, Ozan Oktay, Michiel Schaap, et al.
Introduces attention gates for CNNs that automatically learn to focus on salient regions in medical images, improving classification and segmentation performance without external localization modules.
Jonathon Luiten, Aljos̆a Os̆ep, Patrick Dendorfer, et al.
Introduces HOTA, a unified metric for multi-object tracking that balances detection, association, and localization evaluation.
Ruoqian Lin, Rui Zhang, Chunyang Wang, et al.
This paper presents a deep-learning library and models for robust atom segmentation, localization, denoising, and deblurring in atomic-resolution STEM images, outperforming conventional methods.