OT-Bridge Editor uses geometrically constrained entropic optimal transport to synthesize CAG images with precise stenosis, improving downstream detection by 27.8% on ARCADE and 23.0% on a multi-center dataset.
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Rtmdet: An empirical study of designing real-time object detectors.arXiv preprint arXiv:2212.07784
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SignMAE uses segmentation-driven masking in a mask-and-reconstruct self-supervised task to learn fine-grained sign representations, achieving state-of-the-art accuracy on WLASL, NMFs-CSL, and Slovo with fewer frames and modalities.
KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
A million-box 159-class remote-sensing dataset plus a GSD- and hierarchy-aware detector yields ~5 mAP average gains over fully supervised baselines on nine external benchmarks with no target training.
MORI-Seg learns morphology-aware geometric representations from semantic masks to enable instance segmentation without instance-level annotations.
SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
HMR-Net introduces hierarchical routing with global dataset-level and local scene-level modularity plus conditional experts to improve cross-domain aerial object detection and enable novel category recognition without retraining.
SpikeDet reaches 52.2% AP on COCO 2017 with spiking networks by optimizing firing patterns via MDSNet and SMFM, using half the energy of prior SNN detectors.
Two learned post-processing modules (D2D-Rescore and GossipNet3D) improve mAP, NDS, and true-positive metrics over CircleNMS on nuScenes, especially for small and rare classes.
FDDet is a semi-supervised object detection framework with BBoxMixUp and CGPC that outperforms standard detectors on the new FDD-48 food defect dataset under data-limited real-world conditions.
The OSS Challenge provides benchmarks showing spatiotemporal video models excel at open suturing skill classification and OSATS scoring but struggle with keypoint tracking under occlusion.
JMOF is a new optimization framework for physical adversarial attacks that improves cross-model transferability and enables simultaneous attacks on multiple vision tasks such as object detection and semantic segmentation.
KD-Judge converts unstructured fitness rulebooks into executable kinematic rules via LLM RAG/CoT and validates reps on edge devices, but its reported F1 is based on thresholds fitted to the same CFRep dataset.
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
The report overviews five maritime computer vision benchmark challenges, their datasets, protocols, quantitative results, and top team approaches from the MaCVi 2026 workshop.
The AIM 2025 RipSeg Challenge report presents results from five submissions on single-class instance segmentation of rip currents, highlighting deep learning and domain adaptation techniques on a diverse beach dataset.
citing papers explorer
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Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport
OT-Bridge Editor uses geometrically constrained entropic optimal transport to synthesize CAG images with precise stenosis, improving downstream detection by 27.8% on ARCADE and 23.0% on a multi-center dataset.
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SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition
SignMAE uses segmentation-driven masking in a mask-and-reconstruct self-supervised task to learn fine-grained sign representations, achieving state-of-the-art accuracy on WLASL, NMFs-CSL, and Slovo with fewer frames and modalities.
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KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition
KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
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LEVIRDet: A Million-Scale 159-Category Dataset and Foundation Model for Universal Remote Sensing Object Detection
A million-box 159-class remote-sensing dataset plus a GSD- and hierarchy-aware detector yields ~5 mAP average gains over fully supervised baselines on nine external benchmarks with no target training.
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MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations
MORI-Seg learns morphology-aware geometric representations from semantic masks to enable instance segmentation without instance-level annotations.
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SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection
SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.
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High-Fidelity Single-Image Head Modeling with Industry-Grade Topology
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
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HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images
HMR-Net introduces hierarchical routing with global dataset-level and local scene-level modularity plus conditional experts to improve cross-domain aerial object detection and enable novel category recognition without retraining.
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SpikeDet: Better Firing Patterns for Accurate and Energy-Efficient Object Detection with Spiking Neural Networks
SpikeDet reaches 52.2% AP on COCO 2017 with spiking networks by optimizing firing patterns via MDSNet and SMFM, using half the energy of prior SNN detectors.
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Learned Non-Maximum Suppression for 3D Object Detection
Two learned post-processing modules (D2D-Rescore and GossipNet3D) improve mAP, NDS, and true-positive metrics over CircleNMS on nuScenes, especially for small and rare classes.
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FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
FDDet is a semi-supervised object detection framework with BBoxMixUp and CGPC that outperforms standard detectors on the new FDD-48 food defect dataset under data-limited real-world conditions.
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OSS: Open Suturing Skills Vision-Based Assessment Challenge 2024-2025
The OSS Challenge provides benchmarks showing spatiotemporal video models excel at open suturing skill classification and OSATS scoring but struggle with keypoint tracking under occlusion.
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Towards Universal Physical Adversarial Attacks via a Joint Multi-Objective and Multi-Model Optimization Framework
JMOF is a new optimization framework for physical adversarial attacks that improves cross-model transferability and enables simultaneous attacks on multiple vision tasks such as object detection and semantic segmentation.
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KD-Judge: A Knowledge-Driven Automated Judge Framework for Functional Fitness Movements on Edge Devices
KD-Judge converts unstructured fitness rulebooks into executable kinematic rules via LLM RAG/CoT and validates reps on edge devices, but its reported F1 is based on thresholds fitted to the same CFRep dataset.
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Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
Hausdorff distance-based matching and adaptive query denoising improve Rotated DETR, yielding +4.18 to +4.99 AP50 gains on DOTA-v2.0, DOTA-v1.5, and DIOR-R with ResNet-50.
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Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
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Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
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4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
The report overviews five maritime computer vision benchmark challenges, their datasets, protocols, quantitative results, and top team approaches from the MaCVi 2026 workshop.
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AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
The AIM 2025 RipSeg Challenge report presents results from five submissions on single-class instance segmentation of rip currents, highlighting deep learning and domain adaptation techniques on a diverse beach dataset.