MP-Former: Mask-Piloted Transformer for Image Segmentation (2303.07336v2)

Abstract: We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation. The improvement is based on our observation that Mask2Former suffers from inconsistent mask predictions between consecutive decoder layers, which leads to inconsistent optimization goals and low utilization of decoder queries. To address this problem, we propose a mask-piloted training approach, which additionally feeds noised ground-truth masks in masked-attention and trains the model to reconstruct the original ones. Compared with the predicted masks used in mask-attention, the ground-truth masks serve as a pilot and effectively alleviate the negative impact of inaccurate mask predictions in Mask2Former. Based on this technique, our \M achieves a remarkable performance improvement on all three image segmentation tasks (instance, panoptic, and semantic), yielding $+2.3$AP and $+1.6$mIoU on the Cityscapes instance and semantic segmentation tasks with a ResNet-50 backbone. Our method also significantly speeds up the training, outperforming Mask2Former with half of the number of training epochs on ADE20K with both a ResNet-50 and a Swin-L backbones. Moreover, our method only introduces little computation during training and no extra computation during inference. Our code will be released at \url{https://github.com/IDEA-Research/MP-Former}.

• The paper introduces mask-piloted training using ground-truth masks to guide Transformer decoders, enhancing segmentation consistency.
• It proposes novel metrics—layer-wise mIoU and query utilization—to quantify improvements over conventional Mask2Former.
• Experimental evaluations on datasets like Cityscapes and MS COCO demonstrate significant accuracy gains while reducing training epochs without extra inference cost.

Overview of MP-Former: Mask-Piloted Transformer for Image Segmentation

The paper "MP-Former: Mask-Piloted Transformer for Image Segmentation" introduces a novel approach to enhance the performance and efficiency of image segmentation tasks using Transformers. The authors propose improvements to the Mask2Former, a contemporary Transformer-based framework for image segmentation, by addressing key limitations in its masked-attention mechanism.

Key Contributions

The primary contributions of this work are centered on the development of a mask-piloted training approach, which significantly improves the consistency and accuracy of mask predictions in Transformer decoders. The paper makes several noteworthy contributions:

1. Mask-Piloted Training: The authors propose leveraging ground-truth (GT) masks as attention masks to guide the predictions through decoder layers. This technique, termed mask-piloted (MP) training, aims to mitigate inconsistencies observed in Mask2Former, where predictions might vary significantly between consecutive decoder layers.
2. Innovative Metrics: The researchers introduce novel metrics, namely layer-wise mean Intersection-over-Union (mIoU-L) and layer-wise query utilization (Util), to quantify and analyze the inconsistent predictions issue. These metrics provide insights into how effectively the model utilizes its decoder queries.
3. Comprehensive Evaluation: The MP-Former framework demonstrates substantial improvements across multiple datasets, including ADE20K, Cityscapes, and MS COCO, for diverse segmentation tasks such as instance, semantic, and panoptic segmentation. Notably, experimental results show improvements of +2.3 AP and +1.6 mIoU on the Cityscapes dataset for instance and semantic segmentation tasks, respectively.
4. Computational Efficiency: Despite these advancements in performance, the proposed method introduces minimal additional computational cost during training and no extra cost during inference. Furthermore, the training process is accelerated, requiring nearly half the epochs to achieve comparable or superior results relative to Mask2Former.

Technical Insights

The paper explores the intricacies of Transformer-based segmentation models, identifying that the Mask2Former suffers from sub-optimal mask predictions arising from inconsistent optimization goals across decoder layers. By integrating ground-truth masks into the training loop and adding controlled noise, the MP-Former enhances the ability of the model to refine mask predictions layer by layer more consistently.

The approach is bolstered by techniques including multi-layer mask-piloted training, the introduction of point noises in GT masks, and label-guided training, which collectively focus on improving stability and matching robustness within the Transformer framework.

Implications and Future Directions

The results put forth in this paper have both practical and theoretical implications. Practically, the improvements in training efficiency and segmentation accuracy represent a step forward in deploying Transformers for real-time applications across diverse domains such as autonomous driving, medical imaging, and video analysis. Theoretically, the paper opens new avenues for exploring further architectural tweaks and training paradigms that leverage ground-truth information in novel ways.

Looking forward, potential future research directions could investigate the scalability of this method in even larger datasets and more complex segmentation tasks. Additionally, integrating additional forms of auxiliary information, such as scene geometry or contextual cues, might further boost performance.

In conclusion, the MP-Former exemplifies a significant stride in Transformer-based image segmentation, offering a robust solution to prevalent challenges associated with conventional masked-attention mechanisms. Through its strategic integration of mask-piloted training, this work not only elevates the segmentation accuracy but also sets a precedent for subsequent innovations in Transformer models applied to vision tasks.