SegRAG: Retrieval Augmented Spatial Prompting for Open Vocabulary Semantic Segmentation
cs.CV
Submitted: 2026-05-17
Updated: 2026-09-30
Code: https://github.com/boudiafA/SegRAG
Terminology
Sources
- SAM 2: Segment Anything in Images and Videos
- SAM 3: Segment Anything with Concepts
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- DINOv3
- Fully Convolutional Networks for Semantic Segmentation
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- Panoptic Segmentation
- Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
- Segmenter: Transformer for Semantic Segmentation
- SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
- Per-Pixel Classification is Not All You Need for Semantic Segmentation
- Emerging Properties in Self-Supervised Vision Transformers
- DINOv2: Learning Robust Visual Features without Supervision
- Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching
- Revealing the Semantic Selection Gap in DINOv3 through Training-Free Few-Shot Segmentation
- Learning Transferable Visual Models From Natural Language Supervision
- SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
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