Efficient Self-Ensemble for Semantic Segmentation (2111.13280v2)

Abstract: Ensemble of predictions is known to perform better than individual predictions taken separately. However, for tasks that require heavy computational resources, e.g. semantic segmentation, creating an ensemble of learners that needs to be trained separately is hardly tractable. In this work, we propose to leverage the performance boost offered by ensemble methods to enhance the semantic segmentation, while avoiding the traditional heavy training cost of the ensemble. Our self-ensemble approach takes advantage of the multi-scale features set produced by feature pyramid network methods to feed independent decoders, thus creating an ensemble within a single model. Similar to the ensemble, the final prediction is the aggregation of the prediction made by each learner. In contrast to previous works, our model can be trained end-to-end, alleviating the traditional cumbersome multi-stage training of ensembles. Our self-ensemble approach outperforms the current state-of-the-art on the benchmark datasets Pascal Context and COCO-Stuff-10K for semantic segmentation and is competitive on ADE20K and Cityscapes. Code is publicly available at github.com/WalBouss/SenFormer.

• The paper introduces SenFormer, an efficient self-ensemble method for semantic segmentation that uses multi-scale features from an FPN with independent decoders.
• The method achieves strong empirical performance, including 51.5% mIoU on COCO-Stuff-10K and 64.0% mIoU on Pascal Context, outperforming previous approaches with fewer parameters and lower FLOPs.
• This self-ensemble strategy offers a computationally efficient alternative to traditional ensemble methods, holding practical implications for resource-constrained AI systems and potential generalization to other vision tasks.

Efficient Self-Ensemble for Semantic Segmentation

In this paper, Bousselham et al. introduce a novel approach to enhance the performance of semantic segmentation models while circumventing the high computational costs traditionally associated with ensemble methods. The proposed self-ensemble framework leverages the feature pyramid network (FPN) architecture to exploit multi-scale features, directing them to independent decoders. This essentially creates an ensemble within a singular model, allowing end-to-end training and mitigating cumbersome multi-stage processes typical of conventional ensemble models.

Methodological Innovations

The foundation of this approach is the integration of multiple decoders, each receiving feature sets from different scales of the FPN, as opposed to merging multi-scale feature maps prior to decoding. This not only reduces computational overhead—significantly lowering FLOPs compared to traditional methods such as UperNet—but also purportedly enhances segmentation accuracy by avoiding suboptimal feature fusion strategies. The model, termed SenFormer (Self-ensemble segmentation transFormer), incorporates a transformer-based decoder structure, capitalizing on the capability of transformers to model long-range dependencies—a challenging task for purely convolutional networks.

Performance Evaluation

Empirically, the method demonstrates substantial improvements on several datasets, recording a mean Intersection over Union (mIoU) of 51.5% on COCO-Stuff-10K and 64.0% on Pascal Context, outperforming existing methods by notable margins. Specific notable achievements include a 6 mIoU increase over pyramid feature fusion-based architectures on COCO-Stuff-10K. Additionally, the model's efficiency is highlighted by its lower parameter count and reduced FLOPs while delivering superior or comparable performance.

Theoretical and Practical Implications

From a theoretical perspective, the paper challenges the traditional paradigm of ensemble learning within the field of semantic segmentation. By demonstrating that multiple learners can benefit from shared feature extraction while maintaining independent decoding paths, the paper introduces a potentially more cost-effective ensemble strategy that could be generalized to other vision tasks. Practically, this could influence the design of future AI systems where computational resources are constrained, such as mobile and edge devices.

Future Developments

Consideration of the future impacts and expansions of this work could involve further investigation into the variability and correlation between different decoder paths, as well as exploring the applicability of the self-ensemble strategy within more diverse paradigms, such as real-time video processing or panoptic segmentation. Additionally, examining and optimizing the balance between shared and independent components of the model might yield further improvements in efficiency and performance. Moreover, aligning this self-ensemble framework with emerging architectural concepts like mask classification could further advance its efficacy in complex segmentation tasks.

In conclusion, this paper presents a compelling approach to semantic segmentation that not only improves performance but also brings efficiency to ensemble-like methods, promising avenues for both theoretical exploration and practical deployment in AI-driven applications.