MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary
  Instance Segmentation
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 will be released at
https://github.com/Jiahao000/MosaicFusion.
6511195a73c96e4a4045d298
Jiahao Xie
admin_assigned
2023-09-25T11:00:53.097Z
62fcf93cc1588e1d4c66453e
/avatars/5ebf58aca34e7eb7cb56d1188118e7ed.svg
Jiahao Xie
user
Jiahao000
6511195a73c96e4a4045d299
Wei Li
claimed_verified
2023-09-26T13:02:09.004Z
63bf9f26da08ed0544007446
/avatars/836e857e2eb63fee202e6a5aaa0f9faa.svg
Wei Li
user
weivision
6511195a73c96e4a4045d29a
Xiangtai Li
admin_assigned
2023-09-25T11:02:02.437Z
63958b4414513eaf9029ebf1
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
Xiangtai Li
user
LXT
6511195a73c96e4a4045d29b
Ziwei Liu
admin_assigned
2023-09-25T11:02:13.967Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
6511195a73c96e4a4045d29c
Yew Soon Ong
6511195a73c96e4a4045d29d
Chen Change Loy
MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary
  Instance Segmentation
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 will be released at
https://github.com/Jiahao000/MosaicFusion.
MosaicFusion improves instance segmentation by generating synthetic labeled data through a diffusion-based approach using text-to-image models and cross-attention maps.
diffusion-based data augmentation instance segmentation text-to-image diffusion model cross-attention maps LVIS long-tailed open-vocabulary