Efficient Multi-Grained Cross-modal Alignment for Open-vocabulary Semantic Segmentation from Text Supervision

Core Concepts

The author proposes a Multi-Grained Cross-modal Alignment framework to bridge the alignment granularity gap and achieve efficient open-vocabulary semantic segmentation from text supervision.

Abstract

The paper introduces a novel framework, MGCA, to address the alignment granularity gap in semantic segmentation. It innovatively constructs multi-granular pseudo semantic correspondences and develops an adaptive semantic unit. The method achieves state-of-the-art performance on various benchmarks without dense annotations.
The content discusses the challenges in learning open-vocabulary semantic segmentation and presents a detailed approach to address them. It highlights the importance of fine-grained alignment at object-, region-, and pixel-levels for accurate predictions. The proposed adaptive semantic unit improves segmentation quality significantly.
Key points include the introduction of MGCA framework, the significance of multi-granular alignment, and the development of an adaptive semantic unit. The paper showcases impressive results on multiple datasets without relying on dense annotations.

Stats

8.7 mIoU on ADE20K dataset with 29 million image-text pairs.
Training solely on CC3M dataset with 4.72M learnable parameters.
Achieved new state-of-the-art zero-shot performance on 8 segmentation benchmarks.

Quotes

"Our method achieves substantial advancements over preceding state-of-the-art methods while utilizing a reduced amount of training data."
"Training solely on CC3M datasets with mere 4.72M learnable parameters, we achieve new state-of-the-art zero-shot performance."

Key Insights Distilled From

Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision

by Yajie Liu,Pu... at arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03707.pdf

Deeper Inquiries

How does the proposed adaptive semantic unit compare to traditional segmentation methods

The proposed adaptive semantic unit in the context of open-vocabulary semantic segmentation offers a significant improvement over traditional segmentation methods. Traditional methods often rely on predefined units like groups or pixels, which can lead to under-segmentation or over-segmentation issues. In contrast, the adaptive semantic unit dynamically aggregates semantically relevant pixels based on pixel affinity, forming valid part-level representations. This approach allows for more precise and consistent predictions by leveraging the learned multi-granular alignment capabilities. By combining the advantages of group and pixel units while mitigating their limitations, the adaptive semantic unit enhances segmentation accuracy and quality.

What are the potential limitations or drawbacks of relying solely on web-crawled image-text pairs for training

Relying solely on web-crawled image-text pairs for training does come with potential limitations and drawbacks. One major limitation is the lack of diversity and representativeness in the dataset compared to manually annotated datasets. Web-crawled data may contain biases, inaccuracies, or noise that could impact model performance negatively. Additionally, there may be challenges in ensuring data quality control and label consistency across a large-scale dataset sourced from various online sources. The absence of dense annotations also limits fine-grained alignment during training, potentially affecting the model's ability to capture intricate details required for accurate segmentation tasks.

How might the concept of multi-granular alignment be applied in other areas beyond semantic segmentation

The concept of multi-granular alignment introduced in this study has broader applications beyond semantic segmentation. In natural language processing (NLP), multi-granular alignment could enhance tasks like text summarization by aligning different levels of textual information with corresponding visual elements effectively capturing key content at various granularity levels. In computer vision applications such as object detection or instance segmentation, multi-granular alignment could improve localization accuracy by aligning objects at different scales within an image with corresponding textual descriptions accurately identifying objects' boundaries regardless of size variations.