Visual Prompting for Generalized Few-shot Semantic Segmentation: A Multi-scale Approach

Główne pojęcia

A simple yet effective visual prompting approach for generalized few-shot semantic segmentation that learns visual prompts for both base and novel classes, and utilizes a novel-to-base causal attention mechanism to enhance the novel prompts without deteriorating base class performance.

Streszczenie

The paper proposes a visual prompting approach for generalized few-shot semantic segmentation (GFSS), a more realistic variant of few-shot segmentation where the model is expected to perform well on both base and novel classes.
The key components of the proposed approach are:

Learning visual prompts for both base and novel classes. The novel prompts are initialized using masked average pooling of the support set images.

Introducing a novel-to-base causal attention mechanism that allows the novel prompts to be enriched by the base prompts, without the base prompts being affected. This helps in better distinguishing novel classes from base classes.

Performing multi-scale refinement of the visual prompts, which enables better interaction and reasoning between image features at multiple scales, leading to improved dense predictions.

Extending the approach to a transductive setting, where the visual prompts can be further fine-tuned on the unlabeled test images to improve performance.

The proposed approach achieves state-of-the-art results on the COCO-20i and PASCAL-5i benchmarks for GFSS, outperforming existing inductive and transductive methods. Ablation studies demonstrate the importance of the novel-to-base causal attention and the multi-scale prompt refinement.

Statystyki

The average number of examples per novel class in the support set is k, where k is 1 or 5.
The model is evaluated on 10K test images for COCO-20i and all test images for PASCAL-5i.

Cytaty

"Prompting has been proven to be effective for learning from few demonstrations, as seen in LLMs [5]."
"We posit that prompting within a transformer-based [39] architecture would similarly offer an effective and flexible mechanism for GFSS."

Kluczowe wnioski z

Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach

by Mir Rayat Im... o arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11732.pdf

Głębsze pytania

How can the proposed visual prompting approach be extended to other dense prediction tasks beyond semantic segmentation, such as instance segmentation or panoptic segmentation?

The proposed visual prompting approach can be extended to other dense prediction tasks like instance segmentation or panoptic segmentation by adapting the architecture and methodology to suit the specific requirements of these tasks. Here are some ways to extend the approach:

Instance Segmentation: In instance segmentation, the goal is to detect and segment individual objects within an image. To adapt the visual prompting approach for instance segmentation, the model can be modified to predict instance-specific masks in addition to class labels. This would involve incorporating instance-specific information into the visual prompts and segmentation heads. The model can be trained to differentiate between different instances of the same class by refining the prompts and features at multiple scales.

Panoptic Segmentation: Panoptic segmentation combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. To apply visual prompting to panoptic segmentation, the model needs to handle both stuff (background) and things (objects) classes. The visual prompts can be designed to capture both semantic information for stuff classes and instance-specific details for things classes. The segmentation heads would need to produce both semantic segmentation masks and instance masks.

Feature Fusion: For tasks like instance segmentation and panoptic segmentation, feature fusion plays a crucial role in combining semantic information with instance-specific details. The visual prompting approach can be extended to incorporate feature fusion mechanisms that integrate information from different scales and modalities to improve the segmentation performance.

Transductive Setting: Extending the visual prompting approach to a transductive setting can further enhance the model's ability to generalize to unseen instances or categories. By incorporating transductive fine-tuning techniques that leverage unlabeled test data, the model can adapt to specific instances present in the test images, leading to improved segmentation results.

By customizing the visual prompting approach to suit the requirements of instance segmentation and panoptic segmentation tasks, it can be effectively applied to a broader range of dense prediction tasks beyond semantic segmentation.

How can the proposed visual prompting approach be extended to other dense prediction tasks beyond semantic segmentation, such as instance segmentation or panoptic segmentation?

The proposed visual prompting approach can be extended to other dense prediction tasks like instance segmentation or panoptic segmentation by adapting the architecture and methodology to suit the specific requirements of these tasks. Here are some ways to extend the approach:

Instance Segmentation: In instance segmentation, the goal is to detect and segment individual objects within an image. To adapt the visual prompting approach for instance segmentation, the model can be modified to predict instance-specific masks in addition to class labels. This would involve incorporating instance-specific information into the visual prompts and segmentation heads. The model can be trained to differentiate between different instances of the same class by refining the prompts and features at multiple scales.

Panoptic Segmentation: Panoptic segmentation combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. To apply visual prompting to panoptic segmentation, the model needs to handle both stuff (background) and things (objects) classes. The visual prompts can be designed to capture both semantic information for stuff classes and instance-specific details for things classes. The segmentation heads would need to produce both semantic segmentation masks and instance masks.

Feature Fusion: For tasks like instance segmentation and panoptic segmentation, feature fusion plays a crucial role in combining semantic information with instance-specific details. The visual prompting approach can be extended to incorporate feature fusion mechanisms that integrate information from different scales and modalities to improve the segmentation performance.

Transductive Setting: Extending the visual prompting approach to a transductive setting can further enhance the model's ability to generalize to unseen instances or categories. By incorporating transductive fine-tuning techniques that leverage unlabeled test data, the model can adapt to specific instances present in the test images, leading to improved segmentation results.

By customizing the visual prompting approach to suit the requirements of instance segmentation and panoptic segmentation tasks, it can be effectively applied to a broader range of dense prediction tasks beyond semantic segmentation.

How can the proposed visual prompting approach be extended to other dense prediction tasks beyond semantic segmentation, such as instance segmentation or panoptic segmentation?

The proposed visual prompting approach can be extended to other dense prediction tasks like instance segmentation or panoptic segmentation by adapting the architecture and methodology to suit the specific requirements of these tasks. Here are some ways to extend the approach:

Instance Segmentation: In instance segmentation, the goal is to detect and segment individual objects within an image. To adapt the visual prompting approach for instance segmentation, the model can be modified to predict instance-specific masks in addition to class labels. This would involve incorporating instance-specific information into the visual prompts and segmentation heads. The model can be trained to differentiate between different instances of the same class by refining the prompts and features at multiple scales.

Panoptic Segmentation: Panoptic segmentation combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. To apply visual prompting to panoptic segmentation, the model needs to handle both stuff (background) and things (objects) classes. The visual prompts can be designed to capture both semantic information for stuff classes and instance-specific details for things classes. The segmentation heads would need to produce both semantic segmentation masks and instance masks.

Feature Fusion: For tasks like instance segmentation and panoptic segmentation, feature fusion plays a crucial role in combining semantic information with instance-specific details. The visual prompting approach can be extended to incorporate feature fusion mechanisms that integrate information from different scales and modalities to improve the segmentation performance.

Transductive Setting: Extending the visual prompting approach to a transductive setting can further enhance the model's ability to generalize to unseen instances or categories. By incorporating transductive fine-tuning techniques that leverage unlabeled test data, the model can adapt to specific instances present in the test images, leading to improved segmentation results.

By customizing the visual prompting approach to suit the requirements of instance segmentation and panoptic segmentation tasks, it can be effectively applied to a broader range of dense prediction tasks beyond semantic segmentation.