Context-aware Feature Generation for Zero-shot Semantic Segmentation (2008.06893v1)

Abstract: Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To reduce the annotation pressure, we focus on a challenging task named zero-shot semantic segmentation, which aims to segment unseen objects with zero annotations. This task can be accomplished by transferring knowledge across categories via semantic word embeddings. In this paper, we propose a novel context-aware feature generation method for zero-shot segmentation named CaGNet. In particular, with the observation that a pixel-wise feature highly depends on its contextual information, we insert a contextual module in a segmentation network to capture the pixel-wise contextual information, which guides the process of generating more diverse and context-aware features from semantic word embeddings. Our method achieves state-of-the-art results on three benchmark datasets for zero-shot segmentation. Codes are available at: https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation.

• The paper proposes CaGNet, which integrates a Contextual Module to generate rich features for zero-shot semantic segmentation.
• It leverages category-level semantic embeddings and pixel-wise context to bridge the gap between seen and unseen categories.
• CaGNet demonstrates significant performance gains on benchmark datasets using enhanced segmentation backbones like Deeplabv2.

Context-aware Feature Generation for Zero-shot Semantic Segmentation

The paper "Context-aware Feature Generation for Zero-shot Semantic Segmentation" presents a novel approach aimed at addressing the challenges of zero-shot semantic segmentation (ZSS), a task designed to segment unseen objects without reliance on pixel-wise annotations. By leveraging category-level semantic embeddings, this work introduces a method to bridge the gap between seen and unseen categories, thereby reducing annotation demands.

The authors propose CaGNet, a context-aware feature generation network, which stands out by incorporating contextual information into feature generation—an approach they identify as lacking in earlier methods such as SPNet and ZS3Net. The core component of CaGNet is the Contextual Module (CM), which extracts and encodes pixel-wise contextual information, subsequently guiding the feature generation process. The contextual information is vital for producing diverse and accurate features, especially in helping resolve the mode collapse problem observed in earlier zero-shot learning frameworks.

CaGNet demonstrates superior performance on three benchmark datasets: Pascal-Context, COCO-stuff, and Pascal-VOC. By capturing pixel-wise contextual cues, CaGNet outperforms existing state-of-the-art methods by a significant margin in both harmonic Intersection over Union (hIoU) and mean Intersection over Union (mIoU) metrics. Notably, the proposed architecture effectively balances the trade-off between seen and unseen category segmentation. The paper reports that context-aware feature generation, coupled with strong segmentation backbones like Deeplabv2, markedly improves the ability to generate features that accurately represent unseen categories.

The strong numerical results underscore the effectiveness of incorporating contextual information directly into the feature generation process. Moreover, CaGNet's framework unifies the segmentation backbone with feature generation, allowing for joint training and fine-tuning stages, which further improves segmentation outcomes.

In exploring the implications and future directions, CaGNet provides a promising foundation for advancements in zero-shot learning (ZSL) applied to segmentation tasks. Its approach to generating context-aware features may inspire further exploration into different contextual representations, potentially extending to patch-wise or higher abstraction levels. By doing so, future research can focus on enhancing the diversity and accuracy of generated features even further.

In conclusion, this paper offers substantial contributions to the field of zero-shot semantic segmentation. Through integrating context into the feature generation pipeline, it opens avenues for facilitating efficient learning models that can intelligently and accurately segment unseen objects while minimizing annotation efforts.