# SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation Source: [SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-06-30T05:00:05.200Z Authors: Weichen Zhang, Kebin Liu, Fan Dang, Zhui Zhu, Xikai Sun, Yunhao Liu Published: 2025-08-08T08:26:41.000Z ## Abstract Semantic segmentation in open-vocabulary scenarios presents significant challenges due to the wide range and granularity of semantic categories. Existing weakly-supervised methods often rely on category-specific supervision and ill-suited feature construction methods for contrastive learning, leading to semantic misalignment and poor performance. In this work, we introduce a novel weakly-supervised approach, SynSeg, to address the challenges. SynSeg performs Multi-Category Contrastive Learning (MCCL) as a stronger training signal which robustly injecting intra- and inter-category knowledge during training. We also propose a new feature reconstruction framework named Feature Synergy Structure (FSS). FSS reconstructs discriminative features for contrastive learning through prior fusion and semantic-activation-map enhancement, effectively avoiding the foreground bias introduced by the visual encoder. Furthermore, SynSeg is a lightweight end-to-end solution capable for real-time inference. In general, SynSeg effectively improves the abilities in semantic localization and discrimination under weak supervision in an efficient manner. Extensive experiments on benchmarks demonstrate that our method outperforms state-of-the-art (SOTA) performance, with mIoU score gains ranging from 0.6% up to 8.9% across all reported benchmarks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Pascal Object (val)](): Semantic segmentation; Pascal Object (val); mIoU: 36. - [Semantic Segmentation on COCO Stuff (val)](): Semantic segmentation; COCO Stuff (val); mIoU: 24.5. - [Semantic Segmentation on Cityscapes (val) (mIoU only)](): Semantic segmentation; Cityscapes (val); mIoU: 35.8. - [Semantic Segmentation on ADE20K (val)](): Semantic segmentation; ADE20K (val); mIoU: 18.4. ## Original sources and implementation [Original paper]() [PDF]()