Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2509.01275v2
• Date: Wed, 03 Sep 2025 03:02:25 GMT
• Title: Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation
• Authors: Jiahao Li, Yang Lu, Yachao Zhang, Fangyong Wang, Yuan Xie, Yanyun Qu,
• Abstract summary: We propose X-Agent, an innovative OVSS framework employing latent semantic-aware agent'' to orchestrate cross-modal attention mechanisms.<n>X-Agent achieves state-of-the-art performance while effectively enhancing the latent semantic saliency.
• Score: 48.806000388608005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary semantic segmentation (OVSS) conducts pixel-level classification via text-driven alignment, where the domain discrepancy between base category training and open-vocabulary inference poses challenges in discriminative modeling of latent unseen category. To address this challenge, existing vision-language model (VLM)-based approaches demonstrate commendable performance through pre-trained multi-modal representations. However, the fundamental mechanisms of latent semantic comprehension remain underexplored, making the bottleneck for OVSS. In this work, we initiate a probing experiment to explore distribution patterns and dynamics of latent semantics in VLMs under inductive learning paradigms. Building on these insights, we propose X-Agent, an innovative OVSS framework employing latent semantic-aware ``agent'' to orchestrate cross-modal attention mechanisms, simultaneously optimizing latent semantic dynamic and amplifying its perceptibility. Extensive benchmark evaluations demonstrate that X-Agent achieves state-of-the-art performance while effectively enhancing the latent semantic saliency.

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