A YOLO26 model trained on four leaf segmentation datasets reaches 83.9% mean mAP50-95 on their test sets but only 40.2% on a new 23-species benchmark, revealing substantial cross-domain generalization gaps.
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ZoomSpec achieves 78.1 mAP@0.5:0.95 on the SpaceNet dataset by combining log-space STFT, a coarse proposal net, adaptive heterodyne filtering, and dual-domain fine recognition to improve narrowband visibility in wideband spectrum sensing.
LUSIS-DETR with AquaBSAM reports leading performance on four underwater instance segmentation datasets and real-time FP16 inference on an NVIDIA T4 GPU.
Class-specific diffusion models fine-tuned on 8-24 real images per class generate synthetic data that improves military vehicle detection by up to 8% mAP50 in low-data regimes, with further gains from ControlNet edge conditioning.
RT-SDGDet applies one-to-many supervision, Discriminative Evidence Diversity Learning, and Dual-view Evidence Consistency Learning during training to reduce missed detections in real-time object detectors under unseen domain shifts.
YOLO26 presents a unified real-time vision model family with dual-head end-to-end design, new training components, and task-specific heads that reports improved mAP-latency tradeoffs on COCO and LVIS benchmarks across detection, segmentation, pose, and oriented detection.
A multi-dataset cross-domain knowledge distillation approach improves unified performance on medical image segmentation, classification, and detection by transferring domain-invariant features from a joint teacher model to task-specific students.
Describes a microservice architecture for production document AI pipelines with OCR and LLMs, reporting that OCR dominates latency and GPU inference capacity limits concurrency.
Comparative benchmark finds CNN detectors deliver higher efficiency than transformer detectors for weed detection in tomatoes while transformers capture more context at greater computational cost.
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Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
Describes a microservice architecture for production document AI pipelines with OCR and LLMs, reporting that OCR dominates latency and GPU inference capacity limits concurrency.