MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation
This addresses segmentation in resource-constrained applications like robotics and AR, but it is incremental as it builds on U-Net with attention.
The paper tackled the problem of low-resolution image segmentation by proposing MaskAttn-UNet, which enhances U-Net with a mask attention mechanism to improve accuracy in cluttered scenes, achieving competitive performance on three benchmark datasets at 128x128 resolution with lower computational cost than transformer-based models.
Low-resolution image segmentation is crucial in real-world applications such as robotics, augmented reality, and large-scale scene understanding, where high-resolution data is often unavailable due to computational constraints. To address this challenge, we propose MaskAttn-UNet, a novel segmentation framework that enhances the traditional U-Net architecture via a mask attention mechanism. Our model selectively emphasizes important regions while suppressing irrelevant backgrounds, thereby improving segmentation accuracy in cluttered and complex scenes. Unlike conventional U-Net variants, MaskAttn-UNet effectively balances local feature extraction with broader contextual awareness, making it particularly well-suited for low-resolution inputs. We evaluate our approach on three benchmark datasets with input images rescaled to 128x128 and demonstrate competitive performance across semantic, instance, and panoptic segmentation tasks. Our results show that MaskAttn-UNet achieves accuracy comparable to state-of-the-art methods at significantly lower computational cost than transformer-based models, making it an efficient and scalable solution for low-resolution segmentation in resource-constrained scenarios.