The revenge of BiSeNet: Efficient Multi-Task Image Segmentation
<|reference_start|>The revenge of BiSeNet: Efficient Multi-Task Image Segmentation: 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.<|reference_end|>
@article{rosi2024the,
    title={The revenge of BiSeNet: Efficient Multi-Task Image Segmentation},
    author={Gabriele Rosi, Claudia Cuttano, Niccol`o Cavagnero, Giuseppe Averta,
  Fabio Cermelli},
    journal={arXiv preprint arXiv:2404.09570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09570},
    primaryClass={cs.CV}
}