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MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation

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Abstract

We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Our method is training-free and does not rely on any label supervision. Two key designs enable us to employ an off-the-shelf text-to-image diffusion model as a useful dataset generator for object instances and mask annotations. First, we divide an image canvas into several regions and perform a single round of diffusion process to generate multiple instances simultaneously, conditioning on different text prompts. Second, we obtain corresponding instance masks by aggregating cross-attention maps associated with object prompts across layers and diffusion time steps, followed by simple thresholding and edge-aware refinement processing. Without bells and whistles, our MosaicFusion can produce a significant amount of synthetic labeled data for both rare and novel categories. Experimental results on the challenging LVIS long-tailed and open-vocabulary benchmarks demonstrate that MosaicFusion can significantly improve the performance of existing instance segmentation models, especially for rare and novel categories. Code: https://github.com/Jiahao000/MosaicFusion.

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Data Availability Statements

All data supporting the findings of this study are available online. The LVIS dataset can be downloaded from https://www.lvisdataset.org/dataset. The V3Det dataset can be downloaded from https://v3det.openxlab.org.cn/. The RefCOCO, RefCOCO+, and RefCOCOg datasets can be downloaded from https://github.com/lichengunc/refer. The Stable Diffusion models used to generate data are available at https://github.com/CompVis/stable-diffusion and https://github.com/Stability-AI/generative-models.

Notes

  1. We use the word “single” to encourage the diffusion model to generate a single object at each specific location.

  2. We empirically find that appending a category definition after the category name reduces the semantic ambiguity of generated images due to the polysemy of some category names. For LVIS (Gupta et al., 2019) the category definitions are readily available in annotations, where the meanings are mostly derived from WordNet (Miller, 1995). See Table 2d for ablations on the effect of different prompt templates.

  3. We show more details in the experiment section (see Table 2g, Table 2h, and Fig. 3) that averaging cross-attention maps across all layers and time steps is necessary to achieve the best performance.

  4. The image resolution used during training in Stable Diffusion is \(512\times 512\). We notice that the generated results will get worse if one deviates from this training resolution too much. Thus, we simply choose the aspect ratio of the average LVIS image and keep the longer dimension to 512.

  5. We are aware that different works may use different notations for a \(1\times \) training schedule. In this work, we always refer \(1\times \) schedule to a total of 16 \(\times \) 90k images.

  6. Note that the final number of synthetic images per category is not fixed as we need to filter some images during the mask filtering process introduced in Sect. 3.3. We observe that \(\sim \)50% generated images and masks will be discarded accordingly.

  7. We choose X-Paste for comparison due to its open-sourced implementation. Note that Li et al. (2023) uses a different setting by training and testing models on its own synthetic datasets. Thus, an apple-to-apple quantitative comparison with Li et al. (2023) is infeasible.

  8. We find that the baseline and X-Paste cannot be fully reproduced using the official code (https://github.com/yoctta/XPaste/issues/2). Therefore, our reproduced results are relatively lower than the original reported performance. Nevertheless, all experiments are done under the same settings for a fair comparison.

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Acknowledgements

This study is supported under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). The project is also supported by NTU NAP and Singapore MOE AcRF Tier 2 (MOE-T2EP20120-0001, MOE-T2EP20221-0012).

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  1. S-Lab, Nanyang Technological University, Singapore, Singapore

    Jiahao Xie, Wei Li, Xiangtai Li, Ziwei Liu & Chen Change Loy

  2. Nanyang Technological University, Singapore, Singapore

    Yew Soon Ong

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Xie, J., Li, W., Li, X. et al. MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation. Int J Comput Vis 133, 1456–1475 (2025). https://doi.org/10.1007/s11263-024-02223-3

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