Delving into Shape-aware Zero-shot Semantic Segmentation (2304.08491v1)

Abstract: Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy. However, translating this success to semantic segmentation is not trivial, because this dense prediction task requires not only accurate semantic understanding but also fine shape delineation and existing vision-LLMs are trained with image-level language descriptions. To bridge this gap, we pursue \textbf{shape-aware} zero-shot semantic segmentation in this study. Inspired by classical spectral methods in the image segmentation literature, we propose to leverage the eigen vectors of Laplacian matrices constructed with self-supervised pixel-wise features to promote shape-awareness. Despite that this simple and effective technique does not make use of the masks of seen classes at all, we demonstrate that it out-performs a state-of-the-art shape-aware formulation that aligns ground truth and predicted edges during training. We also delve into the performance gains achieved on different datasets using different backbones and draw several interesting and conclusive observations: the benefits of promoting shape-awareness highly relates to mask compactness and language embedding locality. Finally, our method sets new state-of-the-art performance for zero-shot semantic segmentation on both Pascal and COCO, with significant margins. Code and models will be accessed at https://github.com/Liuxinyv/SAZS.

• The paper introduces a novel SAZS framework that fuses shape-awareness with vision-language alignment for improved zero-shot segmentation.
• It employs unsupervised spectral decomposition and auxiliary boundary detection to refine dense predictions and enhance object boundary delineation.
• Empirical evaluations on PASCAL-5^i and COCO-20^i set new benchmarks, demonstrating significant gains in mIoU and model generalization.

Shape-aware Zero-shot Semantic Segmentation: Advancements and Methodological Insights

The paper "Delving into Shape-aware Zero-shot Semantic Segmentation" focuses on enhancing zero-shot learning capabilities for the task of semantic segmentation by incorporating a novel framework named Shape-Aware Zero-Shot semantic segmentation (SAZS). Leveraging large-scale pre-trained models like CLIP for vision-language tasks, the authors address the challenge of dense prediction required for effective semantic segmentation. This paper's approach provides an innovative methodology to achieve shape-awareness, crucial for delineating fine boundaries of semantic regions that are unseen during the model's training phase.

Key Methodological Contributions

At the core of this research is the integration of shape-awareness into semantic segmentation frameworks. To implement this, the authors use spectral methods that leverage the eigenvectors of Laplacian matrices constructed from self-supervised pixel-wise features. Unlike previous methods aligning predicted edges with ground truth during training, this paper introduces an unsupervised spectral decomposition that focuses on enhancing the segmentation model's awareness of object shapes. Key contributions include:

1. Vision-Language Alignment: The proposed method enforces pixel-wise alignment between visual embeddings generated via DRN or DPT networks and textual embeddings obtained from categories' names using the CLIP text encoder. The collaboration between these different data modalities bridges the semantic gap in zero-shot segmentation.
2. Shape Constraint via Boundary Detection: Shape-awareness is further bolstered by an auxiliary boundary detection task, where features captured by the network's encoder are optimized to adhere to ground truth boundaries using affine transformations. This not only enforces boundary prediction accuracy but also further aligns feature embeddings across modalities.
3. Self-supervised Spectral Decomposition: The use of self-supervised spectral decomposition effectively segments images by deriving eigensegments through an affinity matrix that incorporates both semantic and low-level visual features. When combined with learning-based predictions, this technique significantly enhances the zero-shot segmentation performance by increasing sensitivity to object boundaries.

Experimental Evaluations and Implications

The paper presents experimental results on standard datasets PASCAL-${5}^i$ and COCO-${20}^i$, setting new state-of-the-art performance benchmarks. These results are achieved consistently across different backbone architectures, such as DRN and vision transformers (ViT), showcasing the method’s robustness and adaptability. The empirical evaluation uncovers that shape-awareness not only aids in improving mIoU scores but also enhances the generalization capability of models to unseen categories.

• Practical Implications: The methodology advanced by this paper has immediate applications in areas requiring precise object detection and segmentation in variable environments, like autonomous driving or medical imaging. The zero-shot capability ensures adaptability to evolving scenes without constant retraining.
• Theoretical Implications and Future Directions: This work opens avenues for further exploration into self-supervised learning methods, particularly in integrating shape and language-based features to refine segmentation tasks. Future research could investigate more scalable self-supervised feature learning techniques or expand the SAZS framework to multi-modal sensor data, which often characterize complex environments.

Conclusion

The paper "Delving into Shape-aware Zero-shot Semantic Segmentation" represents a notable advancement in the field of computer vision by improving the integration of shape-awareness into the zero-shot learning paradigm for semantic segmentation. By successfully fusing vision and linguistic modalities and meticulously employing spectral methods, this research delineates a pathway for future breakthroughs in robust, zero-shot segmentation frameworks adaptable to an open set of categories. The integration of spectral techniques with existing deep learning frameworks stands out as a key approach for achieving advancements in precision and generalizability.