SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation (1810.09091v4)

Abstract: One-shot image semantic segmentation poses a challenging task of recognizing the object regions from unseen categories with only one annotated example as supervision. In this paper, we propose a simple yet effective Similarity Guidance network to tackle the One-shot (SG-One) segmentation problem. We aim at predicting the segmentation mask of a query image with the reference to one densely labeled support image of the same category. To obtain the robust representative feature of the support image, we firstly adopt a masked average pooling strategy for producing the guidance features by only taking the pixels belonging to the support image into account. We then leverage the cosine similarity to build the relationship between the guidance features and features of pixels from the query image. In this way, the possibilities embedded in the produced similarity maps can be adapted to guide the process of segmenting objects. Furthermore, our SG-One is a unified framework which can efficiently process both support and query images within one network and be learned in an end-to-end manner. We conduct extensive experiments on Pascal VOC 2012. In particular, our SGOne achieves the mIoU score of 46.3%, surpassing the baseline methods.

• The paper introduces SG-One, which uses masked average pooling and cosine similarity to achieve a 46.3% mIoU on the Pascal VOC 2012 dataset.
• It proposes a unified, end-to-end framework that processes both support and query images simultaneously, reducing redundant parameters.
• The methodology advances resource-efficient segmentation by effectively handling segmentation tasks in settings with scarce annotated data.

SG-One: A Study on One-Shot Semantic Segmentation

The paper "SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation" presents a paper on the challenges and methodologies associated with one-shot image semantic segmentation. The authors delve into the demanding task of segmenting object regions in images of unseen categories by utilizing only a single annotated exemplar. The proposed approach introduces an innovative Similarity Guidance Network (SG-One) designed to efficiently handle the one-shot segmentation problem by leveraging a unified framework capable of processing both support and query images in an end-to-end manner.

In one-shot image semantic segmentation, recognizing and segmenting objects from unseen categories is notoriously difficult due to limited available data. Traditional methods relying on fully annotated datasets are often impractical due to high labeling costs. The SG-One methodology tackles this problem by introducing a novel strategy that uses a masked average pooling mechanism to extract robust object-related representative features from support images. This is followed by employing cosine similarity to structure the relationship between these guidance features and the query image's pixel features.

The authors validate their approach through extensive experiments conducted on the Pascal VOC 2012 dataset. Specifically, the SG-One model achieves a mean Intersection over Union (mIoU) score of 46.3%, demonstrating a significant improvement over baseline methods. This superior performance demonstrates the efficacy of their similarity guidance mechanism in capturing and utilizing relevant information for object segmentation in one-shot scenarios.

The SG-One methodology incorporates novel elements that distinguish it from traditionally used Siamese networks in few-shot learning contexts. It employs a single network capable of simultaneously processing both support and query images, overcoming the redundant parameter usage typical of dual-network approaches. This design choice not only minimizes the risk of overfitting but also enhances the computational efficiency of the model.

Key innovations of SG-One include:

• Masked Average Pooling: The authors propose substituting conventional methods of input manipulation with masked average pooling, which more effectively abstracts object features by negating background influence and avoiding structural changes to network architecture.
• Cosine Similarity for Guidance Maps: The guidance maps derived from cosine similarities between support-object features and query image features are used to direct the segmentation process, resulting in more precise target segmentation.

The paper also explores the transferability of SG-One beyond the field of single-image segmentation to scenarios involving multi-class segmentation and segmentation of video sequences—a comparison highlighting the adaptability of the model under various operational contexts. The researchers critically analyze the framework's utility against other competitive approaches and illustrate the significant gains realized through their model.

The broader implications of this research extend towards the development of more resource-efficient and generalizable segmentation tools within AI, with tangible applications across domains where annotated datasets are scarce or impractical to obtain. The SG-One framework, by demonstrating significant improvements in mIoU scores in constrained data environments, sets a new benchmark for subsequent explorations in one-shot semantic segmentation.

In conclusion, the SG-One approach represents a meaningful contribution to the field of semantic segmentation, particularly in contexts where one-shot learning paradigms are essential. Its novel treatment of guidance feature generation and utilization positions it as a valuable reference for ongoing research in few-shot and minimal-shot learning environments. Future directions may involve expanding its applicability to other forms of image analysis and understanding the interactions between various feature extraction and guidance mechanisms for enhanced segmentation tasks.