M²E-UAV is the first benchmark dataset and evaluation protocol for tiny UAV detection from a moving event camera in motion-on-motion conditions.
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Yolov10: Real-time end-to-end object detection
18 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
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.
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.
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.
Comparative review of YOLOv8 to YOLO11 architectures based on papers, docs, and code inspection, noting incremental improvements and some unchanged blocks.
YOLOv11 adds blocks such as C3k2, SPPF, and C2PSA to improve feature extraction, mAP, and efficiency while supporting detection, segmentation, pose, and oriented detection across model sizes.
citing papers explorer
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SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition
SoftHGNN introduces differentiable soft hyperedges via learnable prototypes and top-k sparse selection to model high-order visual interactions and improve recognition accuracy.
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YOLOv12: Attention-Centric Real-Time Object Detectors
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.
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DFIR-DETR: Frequency-Domain Iterative Refinement and Dynamic Feature Aggregation for Small Object Detection
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.
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Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
YOLOv8 achieves the highest mAP of 80.9% for detecting 15 classes of underwater waste among the tested models.
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YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review
Comparative review of YOLOv8 to YOLO11 architectures based on papers, docs, and code inspection, noting incremental improvements and some unchanged blocks.