ContextFormer: Redefining Efficiency in Semantic Segmentation

TL;DR

ContextFormer introduces a hybrid CNN-Transformer framework for semantic segmentation that balances efficiency, accuracy, and robustness, outperforming existing models on multiple datasets.

Contribution

It proposes a novel hybrid architecture with modules like TPEM, Trans-BDC, and FMM to enhance efficiency and performance in real-time semantic segmentation.

Findings

Achieves state-of-the-art mIoU scores on multiple datasets.

Outperforms existing models in efficiency and accuracy.

Sets new benchmarks for real-time semantic segmentation.

Abstract

Semantic segmentation assigns labels to pixels in images, a critical yet challenging task in computer vision. Convolutional methods, although capturing local dependencies well, struggle with long-range relationships. Vision Transformers (ViTs) excel in global context capture but are hindered by high computational demands, especially for high-resolution inputs. Most research optimizes the encoder architecture, leaving the bottleneck underexplored - a key area for enhancing performance and efficiency. We propose ContextFormer, a hybrid framework leveraging the strengths of CNNs and ViTs in the bottleneck to balance efficiency, accuracy, and robustness for real-time semantic segmentation. The framework's efficiency is driven by three synergistic modules: the Token Pyramid Extraction Module (TPEM) for hierarchical multi-scale representation, the Transformer and Branched DepthwiseConv (Trans-BDC) block for dynamic scale-aware feature modeling, and the Feature Merging Module (FMM) for robust integration with enhanced spatial and contextual consistency. Extensive experiments on ADE20K, Pascal Context, CityScapes, and COCO-Stuff datasets show ContextFormer significantly outperforms existing models, achieving state-of-the-art mIoU scores, setting a new benchmark for efficiency and performance. The codes will be made publicly available upon acceptance.