WUTDet is a 100K-image ship detection dataset with benchmarks indicating Transformer models outperform CNN and Mamba architectures in accuracy and small-object detection for complex maritime environments.
Lw-detr: A transformer replacement to yolo for real-time detection
7 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.
RT-SFOD adapts dual-head detectors like YOLOv10 for source-free object detection via DHF pseudo-label fusion and MARD loss, delivering 1.4-3.5% mAP gains with 1.3x higher throughput and ~2x fewer parameters than prior SFOD methods.
Hippocampus-DETR integrates a hippocampal memory network (HipNet) into DETR to simulate brain subregions for pattern separation, completion, and improved detection accuracy plus generalization.
Echo-α integrates organ-specific detectors with global visual context via an invoke-and-reason agentic loop, trained on a nine-task curriculum plus sequential RL, to achieve superior grounding (56.73%/43.78% F1@0.5) and diagnosis (74.90%/49.20% accuracy) on cross-center renal and breast ultrasound.
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.
citing papers explorer
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WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects
WUTDet is a 100K-image ship detection dataset with benchmarks indicating Transformer models outperform CNN and Mamba architectures in accuracy and small-object detection for complex maritime environments.
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SAM 3: Segment Anything with Concepts
SAM 3 introduces promptable concept segmentation that doubles accuracy of prior systems on images and videos while improving standard SAM segmentation performance.
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Real-Time Source-Free Object Detection
RT-SFOD adapts dual-head detectors like YOLOv10 for source-free object detection via DHF pseudo-label fusion and MARD loss, delivering 1.4-3.5% mAP gains with 1.3x higher throughput and ~2x fewer parameters than prior SFOD methods.
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Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling
Hippocampus-DETR integrates a hippocampal memory network (HipNet) into DETR to simulate brain subregions for pattern separation, completion, and improved detection accuracy plus generalization.
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Echo-{\alpha}: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation
Echo-α integrates organ-specific detectors with global visual context via an invoke-and-reason agentic loop, trained on a nine-task curriculum plus sequential RL, to achieve superior grounding (56.73%/43.78% F1@0.5) and diagnosis (74.90%/49.20% accuracy) on cross-center renal and breast ultrasound.
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RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
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.
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Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
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.