IPLoc-ID extends prior localization-only work to full identification and localization by using a self-posed query in VLMs to reject negative images while preserving comparable localization accuracy.
No time to train! training-free reference-based instance segmentation.arXiv preprint arXiv:2507.02798, 2025
5 Pith papers cite this work. Polarity classification is still indexing.
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Boundary-by-Mask uses a foundation-model encoder plus SDF head to predict boundary-aware distance maps from few mask examples, enabling instance segmentation on low-texture industrial objects via SDF-to-mask reconstruction.
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.
ZODS-RS introduces a zero-training closed-form pipeline using DINOv3 dense features and SAM-style proposals for horizontal-box detection and instance segmentation in remote-sensing imagery.
VocaDet detects arbitrary objects by retrieving multi-granularity visual tokens from a sample-built vector database of position-debiased DINOv3 features and topology, without detector training.
citing papers explorer
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Personalized Object Identification and Localization via In-Context Inference with Vision-Language Models
IPLoc-ID extends prior localization-only work to full identification and localization by using a self-posed query in VLMs to reject negative images while preserving comparable localization accuracy.
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Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts
Boundary-by-Mask uses a foundation-model encoder plus SDF head to predict boundary-aware distance maps from few mask examples, enabling instance segmentation on low-texture industrial objects via SDF-to-mask reconstruction.
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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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ZODS-RS -- Zero-training Oriented Detection & Segmentation for Remote Sensing
ZODS-RS introduces a zero-training closed-form pipeline using DINOv3 dense features and SAM-style proposals for horizontal-box detection and instance segmentation in remote-sensing imagery.
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VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval
VocaDet detects arbitrary objects by retrieving multi-granularity visual tokens from a sample-built vector database of position-debiased DINOv3 features and topology, without detector training.