Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models

• URL: http://arxiv.org/abs/2210.15138v1
• Date: Thu, 27 Oct 2022 02:57:26 GMT
• Title: Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models
• Authors: Chaofan Ma, Yuhuan Yang, Yanfeng Wang, Ya Zhang, Weidi Xie
• Abstract summary: Self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. We introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept. We show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data.
• Score: 39.479912987123214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we investigate simple, yet effective approaches for adapting the pre-trained foundation models to the downstream task of interest, namely, open-vocabulary semantic segmentation. To this end, we make the following contributions: (i) we introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept through a handful of image segmentation data. As a consequence, the model gains the capability of zero-shot transfer to segment novel categories; (ii) without loss of generality, we experiment on a broad range of self-supervised models that have been pre-trained with different schemes, e.g. visual-only models (MoCo v3, DINO), language-only models (BERT), visual-language model (CLIP), and show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data; (iii) we conduct thorough ablation studies to analyze the critical components in our proposed Fusioner, while evaluating on standard benchmarks, e.g. PASCAL-5i and COCO-20i , it surpasses existing state-of-the-art models by a large margin, despite only being trained on frozen visual and language features; (iv) to measure the model's robustness on learning visual-language correspondence, we further evaluate on synthetic dataset, named Mosaic-4, where images are constructed by mosaicking the samples from FSS-1000. Fusioner demonstrates superior performance over previous models.

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