ISTR: End-to-End Instance Segmentation with Transformers (2105.00637v2)

Abstract: End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replacing non-end-to-end components, such as removing non-maximum suppression by training with a set loss based on bipartite matching. However, such an upgrade is not applicable to instance segmentation, due to its significantly higher output dimensions compared to object detection. In this paper, we propose an instance segmentation Transformer, termed ISTR, which is the first end-to-end framework of its kind. ISTR predicts low-dimensional mask embeddings, and matches them with ground truth mask embeddings for the set loss. Besides, ISTR concurrently conducts detection and segmentation with a recurrent refinement strategy, which provides a new way to achieve instance segmentation compared to the existing top-down and bottom-up frameworks. Benefiting from the proposed end-to-end mechanism, ISTR demonstrates state-of-the-art performance even with approximation-based suboptimal embeddings. Specifically, ISTR obtains a 46.8/38.6 box/mask AP using ResNet50-FPN, and a 48.1/39.9 box/mask AP using ResNet101-FPN, on the MS COCO dataset. Quantitative and qualitative results reveal the promising potential of ISTR as a solid baseline for instance-level recognition. Code has been made available at: https://github.com/hujiecpp/ISTR.

Review of "ISTR: End-to-End Instance Segmentation with Transformers"

The paper "ISTR: End-to-End Instance Segmentation with Transformers" introduces a novel method for instance segmentation leveraging the power of Transformers, offering an end-to-end framework that addresses several limitations inherent in traditional approaches. This contribution is significant given the persistent challenges in achieving end-to-end training and inference in instance segmentation tasks.

Key Innovations

The core innovation in this paper is the introduction of the Instance Segmentation Transformer (ISTR), a pioneering approach to enable end-to-end instance segmentation. This method deviates from conventional top-down and bottom-up frameworks by utilizing Transformers to concurrently perform detection and segmentation. By predicting low-dimensional mask embeddings, ISTR facilitates the bipartite matching with ground truth mask embeddings, allowing for effective set loss computation.

ISTR employs a recurrent refinement strategy, which refines predictions iteratively across stages, thereby concurrently enhancing detection and segmentation accuracy. This mechanism diverges from conventional instance segmentation frameworks that rely heavily on object proposal and post-processing mechanisms like non-maximum suppression (NMS).

Numerical Results

The results on the MS COCO dataset demonstrate ISTR's competitive performance. When using ResNet50-FPN, it achieves 46.8/38.6 box/mask Average Precision (AP), and when employing ResNet101-FPN, it attains 48.1/39.9 box/mask AP. These outcomes underscore the robustness of the ISTR framework, showcasing its ability to achieve state-of-the-art results even with suboptimal mask embeddings derived from dimensionality reduction techniques like PCA.

Theoretical and Practical Implications

The theoretical implication of this work is the potential expansion of end-to-end learning paradigms to other high-dimensional output tasks within computer vision. By tackling the dimensionality issue of mask prediction through the encoding of low-dimensional embeddings, this paper offers an insightful strategy for managing complexity in model outputs.

Practically, the framework reduces the reliance on handcrafted components and post-processing steps. The integration of recurrent refinement processes with Transformers could lead to more efficient and accurate models in real-world applications where speed and precision are essential.

Future Directions

While ISTR presents impressive advancements, future research could explore the optimization of mask embeddings beyond PCA to achieve even finer mask predictions. Additionally, extending the framework to integrate attention mechanisms that are more adaptable to various scales, as well as improving the computational efficiency of the recurrent refinement stages, might yield further improvements.

Overall, this paper provides a substantial contribution to the computer vision field by redefining instance segmentation approaches through the use of Transformers. The convergence of detection and segmentation tasks into a unified process could set a new benchmark for future research in instance-level recognition tasks.