IFSeg: Image-free Semantic Segmentation via Vision-Language Model (2303.14396v1)

Abstract: Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates VL-driven artificial image-segmentation pairs and updates a pre-trained VL model to a segmentation task. We construct this artificial training data by creating a 2D map of random semantic categories and another map of their corresponding word tokens. Given that a pre-trained VL model projects visual and text tokens into a common space where tokens that share the semantics are located closely, this artificially generated word map can replace the real image inputs for such a VL model. Through an extensive set of experiments, our model not only establishes an effective baseline for this novel task but also demonstrates strong performances compared to existing methods that rely on stronger supervision, such as task-specific images and segmentation masks. Code is available at https://github.com/alinlab/ifseg.

• The paper introduces an image-free segmentation approach by generating artificial 2D maps using vision-language techniques to replace real-image data.
• The method demonstrates robust performance, achieving +6.9 higher mIoU over MaskCLIP+ on COCO Stuff in zero-shot segmentation.
• The framework reduces the need for extensive labeled datasets, establishing a versatile baseline for VL-driven semantic segmentation across applications.

Overview of the IFSeg Paper: Image-Free Semantic Segmentation via Vision-LLM

The paper introduces IFSeg, a novel methodology for image-free semantic segmentation leveraging vision-language (VL) models. This approach aims to perform semantic segmentation using only target semantic categories without requiring task-specific images or annotations. Unlike traditional segmentation methods that rely on such datasets and labels, IFSeg generates artificial image-segmentation pairs through VL-driven techniques, enhancing the adaptability of pre-trained VL models for segmentation tasks. This paper presents an innovative solution addressing the limitations of existing VL-driven segmentation methods that require additional data and annotations to adapt VL models to downstream tasks.

The primary contribution of this research is the introduction of an image-free segmentation task. The developed IFSeg framework constructs artificial training data by generating 2D maps of random semantic categories and their corresponding word tokens. Given that pre-trained VL models project visual and text tokens into a common space, wherein tokens sharing semantics are located proximally, the artificially generated word maps effectively substitute real images. Through comprehensive experiments, this approach establishes a robust baseline for this task, demonstrating strong performance even compared to methods utilizing more intensive supervision such as task-specific images and segmentation masks.

Key experimental results highlight the efficacy of IFSeg. The model outperforms existing zero-shot and open-vocabulary segmentation methods, achieving improvements on benchmarks like COCO Stuff and ADE20K. Specifically, IFSeg accomplishes +6.9 higher mIoU than MaskCLIP+ in zero-shot segmentation on COCO Stuff, wherein MaskCLIP+ employed 118k training images. When available, conventional scenarios such as supervised or semi-supervised segmentation reveal IFSeg surpasses recent VL-driven segmentation frameworks, including MaskCLIP and DenseCLIP, on benchmarks such as ADE20K.

The implications of adopting IFSeg are manifold. Practically, this technique could reduce reliance on extensive labeled datasets, facilitating the segmentation of novel or uncommon categories. Theoretically, it paves the way for more flexible and adaptable VL models that can generalize across new contexts without specific task-tuning data. Speculatively, future AI developments might leverage this framework to enhance VL model performance in broader applications, extending beyond segmentation into diverse computational areas requiring minimal human annotation.

In conclusion, IFSeg presents a strategic approach to bridge the gap between VL model pre-training and its application in semantic segmentation tasks without direct image-based learning. This work not only highlights the innovative possibility of image-free segmentation but also emphasizes the broader applicability of recent VL models, suggesting a potential shift in how segmentation is conceptually applied within the computer vision domain.