SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
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8 Pith papers cite this work. Polarity classification is still indexing.
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Point cloud geometry is cast as a statistical manifold of per-point Gaussians, with POLI learning the mapping self-supervisedly to improve perception without labeled data.
CAAT selects critical parameters for adversarial robustness in ViTs and applies PEFT to tune only those, yielding a 4.3% robustness drop versus full AT while using ~6% of parameters.
Holi-DETR improves fashion item detection by integrating co-occurrence probabilities, inter-item spatial arrangements, and body keypoint relationships into the DETR architecture.
A 2D Gaussian Splatting method with depth map generation and divide-and-conquer strategy produces high-quality TDOMs and spatial reconstructions without explicit DSM or occlusion detection.
Presents Instant3D for rapid text/image-to-3D generation via multi-view diffusion plus feed-forward reconstruction, and FastMap for 10x faster structure-from-motion with comparable accuracy.
A decoupled prototype matching approach with vision foundation models delivers 6.9% higher average precision than prior training-free methods on industrial few-shot object detection benchmarks.
citing papers explorer
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SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation
SegRAG is a training-free retrieval-augmented framework that extracts class-specific point prompts from a filtered DINOv3 feature bank to boost SAM3 semantic segmentation performance on standard and agricultural benchmarks.
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Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice
Point cloud geometry is cast as a statistical manifold of per-point Gaussians, with POLI learning the mapping self-supervisedly to improve perception without labeled data.
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Efficient Adversarial Training via Criticality-Aware Fine-Tuning
CAAT selects critical parameters for adversarial robustness in ViTs and applies PEFT to tune only those, yielding a 4.3% robustness drop versus full AT while using ~6% of parameters.
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Holi-DETR: Holistic Fashion Item Detection Leveraging Contextual Information
Holi-DETR improves fashion item detection by integrating co-occurrence probabilities, inter-item spatial arrangements, and body keypoint relationships into the DETR architecture.
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High-Quality Spatial Reconstruction and Orthoimage Generation Using Efficient 2D Gaussian Splatting
A 2D Gaussian Splatting method with depth map generation and divide-and-conquer strategy produces high-quality TDOMs and spatial reconstructions without explicit DSM or occlusion detection.
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Efficient 3D Content Reconstruction and Generation
Presents Instant3D for rapid text/image-to-3D generation via multi-view diffusion plus feed-forward reconstruction, and FastMap for 10x faster structure-from-motion with comparable accuracy.
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Decoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
A decoupled prototype matching approach with vision foundation models delivers 6.9% higher average precision than prior training-free methods on industrial few-shot object detection benchmarks.
- The Neglected Baseline in Model Interpretation