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.
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.
KD-Judge structures fitness rules via LLM retrieval and chain-of-thought, then uses pose-guided kinematics for rule-based rep validation with caching for efficient edge deployment, achieving RTF < 1 and speedups up to 15.91x on Jetson.
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.
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.
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.
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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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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KD-Judge: A Knowledge-Driven Automated Judge Framework for Functional Fitness Movements on Edge Devices
KD-Judge structures fitness rules via LLM retrieval and chain-of-thought, then uses pose-guided kinematics for rule-based rep validation with caching for efficient edge deployment, achieving RTF < 1 and speedups up to 15.91x on Jetson.
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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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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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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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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.