# Learning a Semantic Calibration Network for Open-Vocabulary Semantic Segmentation

arXiv: https://arxiv.org/abs/2606.08001 · HTML: https://mlsanity.com/paper/2606.08001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://mlsanity.com/paper/2112.12143/) (2021)
- [Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.04803/) (2023)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP](https://mlsanity.com/paper/2308.02487/) (2023)
- [Decoupling Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2112.07910/) (2021)
- [SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2311.15537/) (2023)
- [Sparse Instance Activation for Real-Time Instance Segmentation](https://mlsanity.com/paper/2203.12827/) (2022)
- [Learning Mask-aware CLIP Representations for Zero-Shot Segmentation](https://mlsanity.com/paper/2310.00240/) (2023)
- [Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2408.00744/) (2024)
- [Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network](https://mlsanity.com/paper/2304.01198/) (2023)
- [Open-Vocabulary Semantic Segmentation with Image Embedding Balancing](https://mlsanity.com/paper/2406.09829/) (2024)
- [Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2412.04533/) (2024)
- [Effective SAM Combination for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2411.14723/) (2024)
- [Generalization Boosted Adapter for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2409.08468/) (2024)
- [High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2412.11464/) (2024)

## Abstract (arXiv)

Semantic image segmentation assigns a predefined category label to each pixel, has achieved significant progress lately. Open-Vocabulary Segmentation (OVS) extends the segmentation task from a fixed set to an open set, enabling the identification and segmentation of novel concepts based on arbitrary text inputs, such as category names or descriptions. In this paper, we propose a novel Semantic Calibration Network (SCN) for open-vocabulary semantic segmentation. Different from prior approaches that focus on feature aggregation or simple fine-tuning of pre-trained models, SCN refines the mask classification process by explicitly modeling the semantic correlations between classes, aiming to enhance the model's discriminative power while effectively preserving the generalization abilities of the pre-trained CLIP model. Specifically, SCN comprises two core components: Class Disambiguation (CD) and Logits Fusion (LF). First, a cross-attention mechanism is utilized to transform the text embeddings into visually aware pseudo-text embeddings, in order to derive an enhanced similarity score that complements the original mask-text similarity score. Subsequently, the Class Disambiguation module captures implicit inter-class dependencies through a residual architecture to effectively resolve semantic ambiguities. Finally, the Logits Fusion module dynamically integrates multifaceted semantic evidence to ensure that the model achieves a robust semantic consensus while maintaining CLIP's inherent generalization capability. Comprehensive experimental results on mainstream benchmarks demonstrate that the proposed method achieves significant performance improvements compared to state-of-the-art algorithms.