SOTR: Segmenting Objects with Transformers (2108.06747v2)

Abstract: Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN). In this work, we present a novel, flexible, and effective transformer-based model for high-quality instance segmentation. The proposed method, Segmenting Objects with TRansformers (SOTR), simplifies the segmentation pipeline, building on an alternative CNN backbone appended with two parallel subtasks: (1) predicting per-instance category via transformer and (2) dynamically generating segmentation mask with the multi-level upsampling module. SOTR can effectively extract lower-level feature representations and capture long-range context dependencies by Feature Pyramid Network (FPN) and twin transformer, respectively. Meanwhile, compared with the original transformer, the proposed twin transformer is time- and resource-efficient since only a row and a column attention are involved to encode pixels. Moreover, SOTR is easy to be incorporated with various CNN backbones and transformer model variants to make considerable improvements for the segmentation accuracy and training convergence. Extensive experiments show that our SOTR performs well on the MS COCO dataset and surpasses state-of-the-art instance segmentation approaches. We hope our simple but strong framework could serve as a preferment baseline for instance-level recognition. Our code is available at https://github.com/easton-cau/SOTR.

• The paper introduces a hybrid framework combining CNN backbones and transformers to directly predict segmentation masks.
• It employs a novel twin attention mechanism that efficiently captures global context while reducing computational overhead.
• SOTR achieves an AP of 40.2% on MS COCO, with improvements for medium (59.0% AP) and large instances (73.0% AP), outperforming traditional methods.

An Expert Review of "SOTR: Segmenting Objects with Transformers"

The paper "SOTR: Segmenting Objects with Transformers" delineates an advanced approach to instance segmentation utilizing a synergistic blend of convolutional neural networks (CNNs) and transformers. This novel framework, Segmenting Objects with Transformers (SOTR), is designed to enhance the accuracy of instance segmentation by leveraging the strengths of each component: CNNs for detailed feature extraction and low-level feature representation, and transformers for capturing long-range dependencies and high-level semantic understanding.

Summary of the Proposed SOTR Framework

SOTR introduces a seamless integration between CNN backbones and transformer models to form a coherent pipeline that predicts segmentation masks directly. This integration allows SOTR to overcome limitations visible in traditional CNN-based instance segmentation, such as inadequate attention to global context and difficulties in long-range semantic inference.

The architecture comprises three core elements:

1. CNN Backbone: This module is tasked with extracting local and low-level features efficiently. The paper supports the efficacy of different backbone depths, with a deeper network, like ResNet-101-FPN, offering notable improvements in accuracy.
2. Transformer Module: At the heart of SOTR's innovation is the twin attention mechanism, a novel self-attention approach designed to reduce computational overhead and memory usage. By focusing on column- and row-attentions separately, the transformer efficiently captures global features without the resource-intensive operation typical of traditional transformer models.
3. Multi-Level Upsampling Module: This module synthesizes the outputs of the CNN and transformer to produce a unified feature map for segmentation tasks. By dynamically predicting convolution kernels for mask generation, this design streamlines operations and improves performance.

Numerical Outcomes and Evaluations

The empirical results on the MS COCO dataset substantiate SOTR's prowess. Employing the ResNet-101-FPN backbone, SOTR achieves an average precision (AP) of 40.2%, surpassing numerous state-of-the-art methods in both accuracy and efficiency. Notably, SOTR excels in addressing medium and large instances, with AP scores of 59.0% and 73.0%, respectively. This outcome is attributed to the effective capturing of global context through the transformer, affirming its superiority over traditional methods like Mask R-CNN and recent models like SOLO and PolarMask.

Implications and Future Directions

The introduction of SOTR presents substantial implications for both theoretical exploration and practical application in computer vision:

• Practical Implications: SOTR could lead to advancements in real-time image processing tasks, including autonomous driving, video surveillance, and robotics, where precise object instance recognition is pivotal.
• Theoretical Contributions: The proposed twin attention and the integration paradigm offer new avenues for enhancing transformer efficiency, encouraging further research into transformer variants across different visual domains.

Looking forward, opportunities for SOTR can involve experimenting with alternative backbone architectures or transformer configurations to further boost performance. Additionally, exploring unsupervised or semi-supervised learning paradigms may extend SOTR’s application across datasets with limited labeled instances.

In conclusion, "SOTR: Segmenting Objects with Transformers" substantiates the viability of combining CNN and transformer methodologies for high-performance instance segmentation. It sets a compelling foundation for future research aimed at streamlining and enhancing instance-level recognition tasks.