title: ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
summary: 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.
paper_authors._id: 64a2300af824e15453d7202a
paper_authors.name: Shuyang Sun
paper_authors._id: 64a2300af824e15453d7202b
paper_authors.name: Weijun Wang
paper_authors._id: 64a2300af824e15453d7202c
paper_authors.name: Qihang Yu
paper_authors._id: 64a2300af824e15453d7202d
paper_authors.name: Andrew Howard
paper_authors._id: 64a2300af824e15453d7202e
paper_authors.name: Philip Torr
paper_authors._id: 64a2300af824e15453d7202f
paper_authors.name: Liang-Chieh Chen
paper_title: ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: mask transformers panoptic segmentation training objective false positive penalization unbalanced loss relaxation techniques MobileNetV3-Small COCO ADE20K Cityscapes Deeplab2