ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask
transformers for efficient panoptic segmentation, democratizing its deployment.
We observe that due to its high complexity, the training objective of panoptic
segmentation will inevitably lead to much higher false positive penalization.
Such unbalanced loss makes the training process of the end-to-end
mask-transformer based architectures difficult, especially for efficient
models. In this paper, we present ReMaX that adds relaxation to mask
predictions and class predictions during training for panoptic segmentation. We
demonstrate that via these simple relaxation techniques during training, our
model can be consistently improved by a clear margin without any extra
computational cost on inference. By combining our method with efficient
backbones like MobileNetV3-Small, our method achieves new state-of-the-art
results for efficient panoptic segmentation on COCO, ADE20K and Cityscapes.
Code and pre-trained checkpoints will be available at
https://github.com/google-research/deeplab2.
64a2300af824e15453d7202a
Shuyang Sun
64a2300af824e15453d7202b
Weijun Wang
64a2300af824e15453d7202c
Qihang Yu
64a2300af824e15453d7202d
Andrew Howard
64a2300af824e15453d7202e
Philip Torr
64a2300af824e15453d7202f
Liang-Chieh Chen
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask
transformers for efficient panoptic segmentation, democratizing its deployment.
We observe that due to its high complexity, the training objective of panoptic
segmentation will inevitably lead to much higher false positive penalization.
Such unbalanced loss makes the training process of the end-to-end
mask-transformer based architectures difficult, especially for efficient
models. In this paper, we present ReMaX that adds relaxation to mask
predictions and class predictions during training for panoptic segmentation. We
demonstrate that via these simple relaxation techniques during training, our
model can be consistently improved by a clear margin without any extra
computational cost on inference. By combining our method with efficient
backbones like MobileNetV3-Small, our method achieves new state-of-the-art
results for efficient panoptic segmentation on COCO, ADE20K and Cityscapes.
Code and pre-trained checkpoints will be available at
https://github.com/google-research/deeplab2.
ReMaX introduces relaxation techniques to improve mask transformer training for panoptic segmentation without extra inference costs, achieving state-of-the-art results with efficient backbones on benchmarks like COCO, ADE20K, and Cityscapes.
mask transformers panoptic segmentation training objective false positive penalization unbalanced loss relaxation techniques MobileNetV3-Small COCO ADE20K Cityscapes Deeplab2