MatMMExtract pipeline creates MatSciFig dataset of 391k annotated materials science figure panels and MaterialScope detection dataset with high accuracy.
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Yolov10: Real-time end-to-end object detection
27 Pith papers cite this work. Polarity classification is still indexing.
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M²E-UAV is the first benchmark dataset and evaluation protocol for tiny UAV detection from a moving event camera in motion-on-motion conditions.
Presents MMIO benchmark and RTVP method achieving state-of-the-art 42.2% AP in zero-shot industrial defect detection.
RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
Releases a recycling-specific dataset of >10k images and evaluates YOLO variants on small dense overlapping objects with augmentation and anomaly detection.
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
DM³-Nav delivers decentralized multi-agent semantic navigation for multimodal open-vocabulary multi-object tasks that matches centralized baselines in simulation and succeeds in real-world robot deployments.
SoftHGNN introduces differentiable soft hyperedges via learnable prototypes and top-k sparse selection to model high-order visual interactions and improve recognition accuracy.
YOLOv12 is a new attention-based real-time object detector that reports higher accuracy than YOLOv10, YOLOv11, and RT-DETR variants at comparable or better speed and efficiency.
PNAFusion proposes pixel-neighborhood cross-attention and adaptive deformable alignment integrated progressively to boost efficiency and accuracy in multispectral object detection.
TinyFormer adds Parallel Bi-fusion Module and Spatial Semantic Adapter to a YOLO-DETR hybrid, raising small-object AP by 1.6 points to 58.5% on MS COCO while keeping real-time speed.
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
Vision-aided deep learning delivers 98.96% beam prediction accuracy and over 98% proactive blockage prediction for mm-wave links, including the first treatment of simultaneous non-uniform mobility.
A scale-robust lightweight CNN for glottis segmentation achieves 92.9% mDice at over 170 FPS with a 19 MB model size on three datasets.
A unified pipeline using OCR, inpainting, and diffusion models restores text in degraded documents on a new synthetic benchmark dataset, evaluated with the proposed UCSM metric.
Introduces UAVDB dataset for UAV detection/segmentation via PIC point-to-box conversion and SAM2 masks, with YOLO baselines showing PIC+SAM2 outperforms prior annotation methods on IoU.
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
A hierarchically decoupled heterogeneous MoE framework with YOLO experts and lightweight gating network reports 76.8% mAP50-95 on a composite traffic sign dataset, a 2.3% gain over baseline with 39.4% lower compute.
Proposes a knowledge-adaptive edge expert agent architecture for sustainable biodiversity monitoring that separates visual perception from reasoning with an explicit knowledge base.
DFIR-DETR augments RT-DETR with frequency-domain iterative refinement and dynamic feature aggregation, reporting 92.9% mAP50 on NEU-DET and 51.6% on VisDrone at 11.7M parameters and 47.2 GFLOPs.
MinerU delivers an open-source pipeline for high-precision document content extraction by integrating specialized models with tuned preprocessing and postprocessing rules.
A multi-objective optimization framework is proposed to assess KPIs in goal-oriented IoT service deployment, with simulation results indicating network efficiency benefits.
YOLO11n achieves the highest mAP@0.5:0.95 of 0.6065 for apple localization, with other detectors showing trade-offs in recall and precision at low confidence thresholds.
YOLOv8 achieves the highest mAP of 80.9% for detecting 15 classes of underwater waste among the tested models.
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A Goal-Oriented Networking Approach for Intelligent IoT Service Deployment
A multi-objective optimization framework is proposed to assess KPIs in goal-oriented IoT service deployment, with simulation results indicating network efficiency benefits.