Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing

TL;DR

This paper introduces Gen4Seg, an automatic pipeline using diffusion models to generate challenging, attribute-edited images for stress-testing semantic segmentation models, revealing robustness limitations and improving performance.

Contribution

We propose a novel attribute editing pipeline with diffusion models to create diverse benchmarks and analyze segmentation model robustness comprehensively.

Findings

Open-vocabulary models are not more robust to geometric variations.

Data augmentation techniques have limited effectiveness against appearance changes.

The pipeline can serve as an effective data augmentation tool to enhance model performance.

Abstract

Semantic segmentation takes pivotal roles in various applications such as autonomous driving and medical image analysis. When deploying segmentation models in practice, it is critical to test their behaviors in varied and complex scenes in advance. In this paper, we construct an automatic data generation pipeline Gen4Seg to stress-test semantic segmentation models by generating various challenging samples with different attribute changes. Beyond previous evaluation paradigms focusing solely on global weather and style transfer, we investigate variations in both appearance and geometry attributes at the object and image level. These include object color, material, size, position, as well as image-level variations such as weather and style. To achieve this, we propose to edit visual attributes of existing real images with precise control of structural information, empowered by diffusion models. In this way, the existing segmentation labels can be reused for the edited images, which greatly reduces the labor costs. Using our pipeline, we construct two new benchmarks, Pascal-EA and COCO-EA. We benchmark a wide variety of semantic segmentation models, spanning from closed-set models to open-vocabulary large models. We have several key findings: 1) advanced open-vocabulary models do not exhibit greater robustness compared to closed-set methods under geometric variations; 2) data augmentation techniques, such as CutOut and CutMix, are limited in enhancing robustness against appearance variations; 3) our pipeline can also be employed as a data augmentation tool and improve both in-distribution and out-of-distribution performances. Our work suggests the potential of generative models as effective tools for automatically analyzing segmentation models, and we hope our findings will assist practitioners and researchers in developing more robust and reliable segmentation models.