ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation (2306.17319v1)

Abstract: 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 \textbf{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 \url{https://github.com/google-research/deeplab2}.

• The paper introduces relaxation methods, ReMask and ReClass, to reduce false positive penalties during panoptic segmentation training.
• ReMask uses an auxiliary semantic branch while ReClass refines class labels, improving Panoptic Quality across benchmarks like COCO and Cityscapes.
• The efficient approach maintains inference speed and paves the way for robust, real-time applications in autonomous driving and robotics.

Overview of ReMaX: Enhancing Efficiency in Panoptic Segmentation

The paper "ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation" presents a methodical approach to enhance the training of mask transformers specifically tailored for panoptic segmentation. This work addresses the inherent complexities and imbalances associated with panoptic segmentation tasks, particularly the issue of false positive penalization during the training phase.

Key Contributions

The authors introduce a mechanism named ReMaX that comprises two main components: Relaxation on Masks (ReMask) and Relaxation on Classes (ReClass). These components are designed to introduce relaxation during the training phase, aiding in the reduction of the disproportionate penalization of false positives. The innovative aspect of ReMaX is its ability to enhance the model's performance without incurring additional computational costs during inference.

ReMask and ReClass Details

• ReMask addresses the imbalance in panoptic segmentation loss by leveraging an auxiliary branch for semantic segmentation. It creates relaxed panoptic predictions, suppressing false positives through semantic masking.
• ReClass adjusts the class labels of predicted masks to account for overlaps with multiple classes, thus accommodating the class prediction complexities inherent in mask transformers.

Numerical Results and Performance

Empirical evaluations demonstrate that ReMaX achieves superior performance across several benchmarks: COCO, ADE20K, and Cityscapes. Notable numerical outcomes include:

• On COCO, ReMaX improves the Panoptic Quality (PQ) to 54.2 with a ResNet-50 backbone over 50K iterations.
• The method achieves a remarkable improvement with MobileNetV3 backbones, enhancing PQ scores by significant margins compared to baselines.

Implications and Future Directions

The simplicitous integration of ReMaX with state-of-the-art frameworks like kMaX-DeepLab underscores a promising direction for efficient segmentation tasks. Practically, this can translate into more robust and efficient applications in real-time scenarios like autonomous driving and robotics. Theoretically, ReMaX encourages further exploration into adaptive relaxation techniques tailored for complex model training.

A speculative future direction could involve extending ReMaX to other transformer-based architectures and exploring its potential impact on computational efficiency and convergence stability across diverse AI applications. The combination of theoretical insight and empirical results presented in this paper forms a strong basis for future work in relaxing learning objectives for sophisticated machine learning models.