CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement (2005.02551v1)

Abstract: State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.

• The paper introduces CascadePSP, a cascade refinement network that integrates global semantic structure with local boundary details to enhance segmentation accuracy.
• It refines outputs from existing models using a multi-scale pyramid pooling mechanism and a combination of cross-entropy and L1+L2 losses for improved precision.
• Experimental results on datasets like PASCAL VOC, BIG, and ADE20K demonstrate substantial gains in IoU and mean Boundary Accuracy for high-resolution segmentation.

CascadePSP: Advancing High-Resolution Semantic Segmentation

The paper "CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement" proposes a novel method for tackling challenges in high-resolution image segmentation without the necessity of high-resolution training data. The authors introduce CascadePSP, a refinement network that enhances segmentation accuracy across different scales. The network operates efficiently on input segmentations produced by other models, refining outputs to achieve pixel-level precision with markedly improved boundary delineation.

Problem Context

Traditional semantic segmentation methods face significant hurdles when applied to high-resolution images, such as 4K UHD, due to memory and computational constraints. Common techniques involving bicubic upsampling cannot satisfactorily capture the intricacies of object boundaries. CascadePSP addresses this by leveraging a class-agnostic approach that refines segmentation output through global and local processes.

Methodology

The proposed CascadePSP utilizes a cascade design to progressively improve segmentation masks from coarse to fine resolutions. It employs a Refinement Module (RM) that integrates information from different segmentation scales, allowing the network to balance global semantic structure with boundary detail. The refinement process incorporates multiple levels, from output strides of 8 to 1, ensuring robust contextual feature capture.

The network's architecture is underpinned by a pyramid pooling mechanism adapted from PSPNet, allowing consistent handling of varying input resolutions. Multi-level input segmentation maps are upsampled and combined with image data to generate refined outputs. The use of a cross-entropy and L1+L2 loss combination improves both global and boundary accuracy without overfitting to specific datasets or models.

Experimental Evaluation

The authors validate CascadePSP's efficacy using the PASCAL VOC 2012, BIG, and ADE20K datasets. The method notably enhances results with improvements in Intersection over Union (IoU) and a newly introduced mean Boundary Accuracy (mBA) metric. Notably, the network achieves high-resolution segmentation on the BIG dataset, showcasing its ability to refine segmentations beyond the resolution seen during training.

The ablation studies underscore the necessity of the cascade design and the specified loss functions, highlighting improvements over baseline segmentations generated by models like DeepLabV3+ and PSPNet.

Implications and Future Work

CascadePSP represents a significant step toward flexible and scalable high-resolution segmentation solutions. Its class-agnostic nature and ability to refine diverse segmentation outputs facilitate its integration into existing workflows, potentially making it highly applicable in real-world scenarios requiring high accuracy.

Future work could extend this approach to explore more complex, multi-object scenarios without relying on separate networks for each class. Advancing this methodology to incorporate additional contextual information from diverse modalities could further enhance segmentation refinement capabilities.

In conclusion, the CascadePSP framework innovatively addresses existing gaps in high-resolution image segmentation. By maintaining computational efficiency and achieving high boundary precision, this approach presents a meaningful advancement for applications in computer vision that require detailed image analysis.