# LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation

arXiv: https://arxiv.org/abs/2503.19777 · HTML: https://mlsanity.com/paper/2503.19777/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 56 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 30 of 46)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://mlsanity.com/paper/2112.12143/) (2021)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2211.14813/) (2022)
- [Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs](https://mlsanity.com/paper/2212.00785/) (2022)
- [SuS-X: Training-Free Name-Only Transfer of Vision-Language Models](https://mlsanity.com/paper/2211.16198/) (2022)
- [Aligning Bag of Regions for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2302.13996/) (2023)
- [Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning](https://mlsanity.com/paper/2212.04994/) (2022)
- [ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference](https://mlsanity.com/paper/2407.12442/) (2024)

## Built on by (most cited first, 21 of 21)

- [TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models](https://mlsanity.com/paper/2505.23769/) (2025)
- [REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion](https://mlsanity.com/paper/2512.16636/) (2025)
- [Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2602.06333/) (2026)
- [PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2603.21528/) (2026)
- [VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference](https://mlsanity.com/paper/2605.12325/) (2026)
- [Standalone DINOv3 for Training-Free Open-Vocabulary Semantic Segmentation in Remote Sensing](https://mlsanity.com/paper/2608.03023/) (2026)
- [Semi-Supervised Semantic Segmentation via Derivative Label Propagation](https://mlsanity.com/paper/2508.02254/) (2025)
- [Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders](https://mlsanity.com/paper/2509.00177/) (2025)
- [ARM: A Learnable, Plug-and-Play Module for CLIP-based Open-vocabulary Semantic Segmentation](https://mlsanity.com/paper/2512.24224/) (2025)
- [Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?](https://mlsanity.com/paper/2602.23339/) (2026)
- [IGLOSS: Image Generation for Lidar Open-vocabulary Semantic Segmentation](https://mlsanity.com/paper/2604.01361/) (2026)
- [SPAR: Single-Pass Any-Resolution ViT for Open-vocabulary Segmentation](https://mlsanity.com/paper/2604.02252/) (2026)
- [Direct Segmentation without Logits Optimization for Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2604.07723/) (2026)
- [MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models](https://mlsanity.com/paper/2604.14582/) (2026)
- [SENSE: Stereo OpEN Vocabulary SEmantic Segmentation](https://mlsanity.com/paper/2604.15946/) (2026)
- [Benchmarking Composed Image Retrieval for Applied Earth Observation](https://mlsanity.com/paper/2605.24442/) (2026)
- [Geometry-Preserving Unsupervised Alignment for Heterogeneous Foundation Models](https://mlsanity.com/paper/2606.04385/) (2026)
- [Training-Free Generalized Few-Shot Segmentation through Open-Vocabulary Semantic Arbitration](https://mlsanity.com/paper/2606.09474/) (2026)
- [LASA: A Weak Supervision Method for Open-Vocabulary Scene Sketch Semantic Segmentation](https://mlsanity.com/paper/2606.11837/) (2026)
- [ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3](https://mlsanity.com/paper/2606.16996/) (2026)
- [TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving](https://mlsanity.com/paper/2607.04812/) (2026)

## Abstract (arXiv)

We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs through label propagation, which jointly optimizes predictions by incorporating patch-to-patch relationships. Since VLMs are primarily optimized for cross-modal alignment and not for intra-modal similarity, we use a Vision Model (VM) that is observed to better capture these relationships. We address resolution limitations inherent to patch-based encoders by applying label propagation at the pixel level as a refinement step, significantly improving segmentation accuracy near class boundaries. Our method, called LPOSS+, performs inference over the entire image, avoiding window-based processing and thereby capturing contextual interactions across the full image. LPOSS+ achieves state-of-the-art performance among training-free methods, across a diverse set of datasets. Code: https://github.com/vladan-stojnic/LPOSS