SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers (2306.06289v2)

Abstract: This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}. In this study, we introduce a novel Attention-to-Mask (\atm) module to design a lightweight decoder effective for plain ViT. The proposed ATM converts the global attention map into semantic masks for high-quality segmentation results. Our decoder outperforms the popular decoder UPerNet using various ViT backbones while consuming only about $5\%$ of the computational cost. For the encoder, we address the concern of the relatively high computational cost in the ViT-based encoders and propose a \emph{Shrunk++} structure that incorporates edge-aware query-based down-sampling (EQD) and query-based upsampling (QU) modules. The Shrunk++ structure reduces the computational cost of the encoder by up to $50\%$ while maintaining competitive performance. Furthermore, we propose to adapt SegViT for continual semantic segmentation, demonstrating nearly zero forgetting of previously learned knowledge. Experiments show that our proposed SegViTv2 surpasses recent segmentation methods on three popular benchmarks including ADE20k, COCO-Stuff-10k and PASCAL-Context datasets. The code is available through the following link: \url{https://github.com/zbwxp/SegVit}.

An Evaluation of SegViT v2: Efficient Semantic Segmentation with Vision Transformers

The paper "SegViT v2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers" examines the utility of using plain Vision Transformers (ViTs) as a robust alternative to traditional convolutional neural network-based frameworks for semantic segmentation tasks. SegViT v2 introduces an innovative architectural framework, including both encoder and decoder components, designed to harness the capabilities of ViTs with improvements in computational efficiency and robustness against catastrophic forgetting, characteristic of continual learning paradigms.

The authors propose a refined decoder design, the Attention-to-Mask (ATM) module, which efficiently maps global attention derived from ViTs into meaningful semantic masks, minimizing computational overhead to approximately 5% of total processing cost compared to prior models like UPerNet. ATM leverages the cross-attention mechanism to derive class-specific segmentation masks directly from token similarity maps, highlighting its capability to encapsulate semantic context without convoluted per-pixel classifications.

Moreover, for the encoder component, the paper explores a Shrunk++ structure to mitigate the high computational demands intrinsic to ViTs. This novel structure integrates strategies such as edge-aware query-based downsampling (EQD) and query upsampling, resulting in a halving of computational expenses while maintaining performance competitive with state-of-the-art models.

Experimental validation confirms SegViT v2's effectiveness across multiple established benchmarks, including ADE20k, COCO-Stuff-10k, and PASCAL-Context, achieving superior segmentation performance with a measurable reduction in computational costs. For instance, SegViT-Shrunk-BEiT v2 Large exhibits successful scalability, achieving an mIoU of 55.7% on ADE20K with a considerable reduction in GFLOPs compared to UPerNet.

An intriguing characteristic of SegViT v2 is its apparent resilience to catastrophic forgetting, a critical challenge in continual learning scenarios. The authors demonstrate that by embracing the inherent strengths of ViTs for representation learning, new classifiers (via new ATM modules) can be seamlessly integrated without detrimental effects on previously learned data. Conclusively, experiments on complex datasets like ADE20k under continual learning protocols reveal SegViT v2 nearly eliminates forgetting, improving the predictive accuracy and versatility across incrementally acquired tasks.

The theoretical implications of SegViT v2 are substantial as they suggest a pivotal shift towards utilizing the representational power of transformers over traditional hierarchical neural models for semantic segmentation. The structure also invites potential future improvements, such as enhanced integration with foundation models and adaptability to varied data domains.

Continually, ongoing research could extend SegViT's application across an even broader spectrum of vision-based tasks and further explore the intricate interplay between fine-tuning self-supervised representations and continual adaptation in dynamic environments. As the use of Vision Transformers in high-dimensional, dense prediction tasks matures, SegViT v2 stands as a compelling advance indicating efficient and robust AI model development's future trajectory.