The revenge of BiSeNet: Efficient Multi-Task Image Segmentation
Gabriele Rosi Claudia Cuttano Niccolò Cavagnero Giuseppe Averta Fabio Cermelli
Recent advancements in image segmentation have focused on enhancing the
efficiency of the models to meet the demands of real-time applications,
especially on edge devices. However, existing research has primarily
concentrated on single-task settings, especially on semantic segmentation,
leading to redundant efforts and specialized architectures for different tasks.
To address this limitation, we propose a novel architecture for efficient
multi-task image segmentation, capable of handling various segmentation tasks
without sacrificing efficiency or accuracy. We introduce BiSeNetFormer, that
leverages the efficiency of two-stream semantic segmentation architectures and
it extends them into a mask classification framework. Our approach maintains
the efficient spatial and context paths to capture detailed and semantic
information, respectively, while leveraging an efficient transformed-based
segmentation head that computes the binary masks and class probabilities. By
seamlessly supporting multiple tasks, namely semantic and panoptic
segmentation, BiSeNetFormer offers a versatile solution for multi-task
segmentation. We evaluate our approach on popular datasets, Cityscapes and
ADE20K, demonstrating impressive inference speeds while maintaining competitive
accuracy compared to state-of-the-art architectures. Our results indicate that
BiSeNetFormer represents a significant advancement towards fast, efficient, and
multi-task segmentation networks, bridging the gap between model efficiency and
task adaptability.