SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation

แนวคิดหลัก

The author proposes the SemPLeS framework to enhance weakly-supervised semantic segmentation by leveraging CLIP latent space and prompt learning techniques.

บทคัดย่อ

The SemPLeS framework introduces Contrastive Prompt Learning and Prompt-guided Semantic Refinement to improve semantic alignment between object regions and target categories, achieving state-of-the-art performance on standard benchmarks. The method effectively suppresses co-occurring backgrounds without manual prompting efforts.

สถิติ

The proposed SemPLeS framework achieves 68.7% mIoU in initial activation maps and 78.4% mIoU in pseudo masks.
SemPLeS outperforms previous methods with 83.4% mIoU on PASCAL VOC and 56.1% mIoU on MS COCO.

คำพูด

"Our learned prompts properly capture and suppress co-occurring backgrounds for each object category."
"SemPLeS successfully generates pixel-wise predictions from image-level supervision."

ข้อมูลเชิงลึกที่สำคัญจาก

SemPLeS

by Ci-Siang Lin... ที่ arxiv.org 03-12-2024

https://arxiv.org/pdf/2401.11791.pdf

สอบถามเพิ่มเติม

How does the SemPLeS framework compare to fully supervised semantic segmentation methods

The SemPLeS framework for weakly-supervised semantic segmentation compares to fully supervised methods in several key ways. While fully supervised semantic segmentation methods require precise pixel-level annotations for training, SemPLeS operates with only image-level supervision, making it more efficient and cost-effective. Additionally, SemPLeS leverages the CLIP latent space to enhance semantic alignment between segmented regions and target object categories, allowing for better generalization across different datasets and categories. However, one limitation compared to fully supervised methods is that the quality of segmentation may not be as high due to the lack of detailed annotations.

What are the potential limitations of relying on CLIP latent space for semantic segmentation

Relying on the CLIP latent space for semantic segmentation can have some potential limitations. One limitation is that CLIP may not capture all nuances or specific details required for fine-grained segmentation tasks since it learns from a large corpus of diverse image-text pairs rather than task-specific data. Additionally, there may be challenges in interpreting and understanding how prompts are learned within the CLIP model, which could impact the effectiveness of prompt-guided techniques in certain scenarios. Furthermore, relying solely on CLIP may limit flexibility in adapting to new or specialized domains where pre-trained knowledge might not be as relevant.

How can prompt learning techniques be applied to other computer vision tasks beyond segmentation

Prompt learning techniques can be applied beyond semantic segmentation to various computer vision tasks by leveraging vision-language models like CLIP. For instance:

In object detection: Prompt learning can help improve object localization accuracy by guiding detectors towards specific regions related to class labels.
In image captioning: Prompts can assist in generating more contextually relevant captions based on visual content.
In visual question answering (VQA): By using prompts tailored to VQA tasks, models can provide more accurate answers based on both images and questions.
By incorporating prompt learning into these tasks, models can benefit from enhanced contextual understanding and improved performance across a range of vision-based applications.