Leveraging Point Prompting and Curriculum Learning for Weakly-Supervised Referring Image Segmentation

Grunnleggende konsepter

A novel point prompting framework (PPT) that leverages curriculum learning and object-centric images to enable effective integration of frozen CLIP and SAM models for weakly-supervised referring image segmentation.

Sammendrag

The paper presents an innovative framework, Point PrompTing (PPT), for weakly-supervised referring image segmentation (RIS). The core of PPT is a point generator that harnesses CLIP's text-image alignment capability and SAM's powerful mask generation ability, while also generating negative point prompts to address the issues of noisy and excessive focus on object parts.
To address these challenges, the authors introduce a curriculum learning strategy that progressively transitions from class-based segmentation to complex referring image segmentation, incorporating factors such as location and relationships. Additionally, the authors leverage object-centric images from ImageNet to help the point generator learn semantic-aware and comprehensive point prompts, as opposed to merely salient ones.
The authors' experiments demonstrate that their PPT significantly and consistently outperforms prior weakly supervised RIS techniques, achieving an average mIoU improvement of 11.34%, 14.14%, and 6.97% on RefCOCO, RefCOCO+, and G-Ref datasets, respectively. The authors also show that their approach significantly improves precision at different IoU thresholds compared to other weakly supervised methods.

Statistikk

"The model achieves an average mIoU improvement of 11.34%, 14.14%, and 6.97% on RefCOCO, RefCOCO+, and G-Ref datasets, respectively, compared to prior weakly supervised RIS techniques."
"The model significantly improves precision at different IoU thresholds compared to other weakly supervised methods, with a 25.2% and 27.2% improvement at prec@0.5 and prec@0.7, respectively."

Sitater

"Our PPT consistently outperforms all other weakly supervised RIS methods, achieving an average mIoU improvement of 11.34%, 14.14%, and 6.97% on RefCOCO, RefCOCO+, and G-Ref, respectively, over the previous highest performance."
"Compared to TRIS [37], which shares a strategy of first extracting pseudo-labels and then training with us, we achieve significant improvements in mIoU on the three datasets, with gains of 15.59%, 14.44%, and 6.97%, respectively."

Viktige innsikter hentet fra

Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation

by Qiyuan Dai,S... klokken arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11998.pdf

Dypere Spørsmål

How can the proposed point prompting framework be extended to other vision-language tasks beyond referring image segmentation

The proposed point prompting framework can be extended to other vision-language tasks beyond referring image segmentation by adapting the point generator to suit the specific requirements of the task at hand. For tasks like visual question answering (VQA), the point generator can be trained to generate points that correspond to relevant regions in the image based on the question asked. This can help the model focus on the specific areas of the image that are relevant to answering the question accurately. Similarly, for tasks like image captioning, the point generator can be used to prompt the model to focus on key objects or regions in the image that need to be described in the caption. By customizing the point generator and integrating it into different vision-language models, the framework can be applied to a wide range of tasks that require understanding and generating language descriptions based on visual inputs.

What are the potential limitations of the curriculum learning strategy, and how could it be further improved to handle more complex relationships between objects and text

One potential limitation of the curriculum learning strategy is the challenge of handling more complex relationships between objects and text, especially in scenarios where the relationships are nuanced or context-dependent. To address this limitation and further improve the strategy, several enhancements can be considered:

Dynamic Curriculum: Implementing a dynamic curriculum learning approach where the model adapts the difficulty of the training samples based on its learning progress. This adaptive strategy can help the model focus more on challenging samples as it becomes more proficient in simpler tasks.
Multi-level Curriculum: Introducing multiple levels of curriculum learning where the model progresses through different levels of complexity gradually. This can help the model learn hierarchical relationships between objects and text, starting from simple relationships and gradually moving to more complex ones.
Feedback Mechanisms: Incorporating feedback mechanisms that provide the model with information on its performance on specific types of relationships. This feedback can guide the curriculum learning process and help the model prioritize learning tasks that are more challenging or critical for performance improvement.

By incorporating these enhancements, the curriculum learning strategy can be further optimized to handle more complex relationships between objects and text in vision-language tasks.

Could the integration of object-centric images be generalized to other types of auxiliary data sources to further enhance the performance of weakly-supervised referring image segmentation

The integration of object-centric images can be generalized to other types of auxiliary data sources to further enhance the performance of weakly-supervised referring image segmentation by leveraging datasets that provide rich semantic information and diverse object representations. Some ways to extend this integration include:

Domain-specific Data: Incorporating domain-specific datasets that contain specialized object categories or contextual information relevant to the task at hand. This can help the model learn specific object relationships and improve segmentation accuracy in domain-specific scenarios.
Multi-modal Data: Integrating multi-modal datasets that combine images with other modalities such as audio or text. By training the model on diverse data sources, it can learn to extract meaningful relationships between different modalities and improve its understanding of complex scenes.
Fine-grained Annotations: Utilizing datasets with fine-grained annotations that provide detailed object boundaries or attributes. This can help the model generate more precise segmentation masks and improve its ability to capture subtle object relationships in referring image segmentation tasks.

By exploring and incorporating a variety of auxiliary data sources, the performance of weakly-supervised referring image segmentation can be significantly enhanced, leading to more accurate and robust segmentation results.