Learnable Prompts for Generalized Few-Shot Semantic Segmentation in Remote Sensing

Core Concepts

A simple yet effective method to handle novel class prediction in few-shot setting using learnable prompts, combined with techniques like patch-and-stitch and novel class filtering to boost performance on the OpenEarthMap Land Cover Mapping Few-Shot Challenge.

Abstract

The paper presents a method for addressing the Generalized Few-Shot Semantic Segmentation (GFSS) problem in the remote sensing domain. The key aspects of the approach are:

Base Classes Training:

The base classes are trained using a Masked Image Modeling (MIM) approach, where the model learns to reconstruct masked regions of the input image by leveraging contextual information.
Two masking strategies are used - random masking and half masking, depending on whether the prompt and target images contain the same classes or not.

Novel Classes Handling:

For novel classes, the authors use learnable prompts that are optimized independently for each novel class using the limited support samples.
This allows the model to adapt to the characteristics of each novel class without compromising the performance on the base classes.
The learnable prompts are optimized in two phases - first using only the patches containing the novel class, and then using all patches to mitigate false positive predictions.

Inference Techniques:

Image similarity search is used to retrieve the top-k most similar images to the target image, which are then used as the prompt to improve the prediction quality.
A patch-and-stitch approach is introduced to handle varying object sizes in remote sensing imagery. The image is divided into non-overlapping patches, and the predictions are stitched together using an inpainting-based technique to address discontinuities along patch boundaries.
Novel class filtering is performed based on the similarity between the target image and the support samples to reduce false positive predictions.

The proposed method achieves a weighted mIoU of 35.08 on the validation set and 36.52 on the test set of the OpenEarthMap Land Cover Mapping Few-Shot Challenge, demonstrating significant improvements over the baseline SegGPT model.

Stats

The base classes mIoU of the fine-tuned SegGPT model is 15.96.
Using similar image prompts improves the mIoU to 17.82.
Incorporating learnable prompts for novel classes increases the mIoU to 25.26.
The patch-and-stitch technique further boosts the mIoU to 29.41.
Filtering novel class predictions based on image similarity leads to a final mIoU of 35.08 on the validation set.

Quotes

"The reason we chose this is due to the emergence of new foundation models with strong generalization capabilities [12, 23, 32]. The prompt for each novel class serves as an adaptation layer to handle a specific novel class characteristics."
"To this end, we use a learnable prompt Z which acts as Xp and Yp. After we train the model on the base classes, we freeze the whole model and optimize only Z."
"The main strength of this approach is that the introduction of novel classes does not compromise the performance of the base classes."

Key Insights Distilled From

Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain

by Steve Andrea... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10307.pdf

Deeper Inquiries

How can the performance of the proposed method be further improved, especially on challenging classes like bareland

To improve the performance of the proposed method, especially on challenging classes like bareland, several strategies can be implemented:

Data Augmentation: Increasing the diversity of training data through augmentation techniques like rotation, scaling, and flipping can help the model generalize better to challenging classes like bareland.

Class Balancing: Since classes like bareland may have fewer samples compared to others, balancing the class distribution in the training data can help the model learn these classes more effectively.

Fine-tuning Hyperparameters: Tuning hyperparameters such as learning rate, batch size, and optimizer settings can have a significant impact on the model's ability to learn complex classes like bareland.

Feature Engineering: Introducing additional features or pre-processing techniques specific to bareland can provide the model with more discriminative information for better segmentation.

Ensemble Methods: Combining the predictions of multiple models or variations of the same model can help improve overall performance, especially on challenging classes.

By implementing these strategies and potentially exploring class-specific optimization techniques, the performance on challenging classes like bareland can be enhanced.

What other foundation models beyond SegGPT could be explored for the GFSS task in remote sensing, and how would they impact the overall results

Exploring other foundation models beyond SegGPT for the GFSS task in remote sensing can offer different advantages and impacts on the overall results:

ViT (Vision Transformer): ViT has shown promising results in various computer vision tasks and could potentially offer a different perspective on semantic segmentation in remote sensing. Its attention mechanism can capture long-range dependencies effectively.

ResNet (Residual Network): ResNet architectures are known for their depth and skip connections, which can help in learning intricate features in remote sensing imagery. Fine-tuning a pre-trained ResNet model could provide strong baseline performance.

EfficientNet: EfficientNet models are known for their efficiency in terms of parameter size and computational cost. They could be beneficial for resource-constrained environments while maintaining competitive performance.

DenseNet: DenseNet's dense connectivity pattern can facilitate feature reuse and gradient flow, potentially improving the model's ability to capture intricate details in remote sensing images.

By exploring these foundation models and adapting them to the GFSS task in remote sensing, researchers can gain insights into how different architectures impact segmentation performance and generalization capabilities.

How can the learnable prompts be made more efficient, both in terms of memory footprint and optimization time, to enable real-time deployment in practical applications

To make learnable prompts more efficient for real-time deployment in practical applications, the following optimizations can be considered:

Knowledge Distillation: Utilizing knowledge distillation techniques to compress the learnable prompts while retaining their essential information can reduce the memory footprint without compromising performance.

Quantization: Applying quantization methods to the learnable prompts can reduce the precision of the parameters, leading to a smaller memory footprint and faster inference without significant loss in accuracy.

Sparsity Techniques: Leveraging sparsity-inducing techniques to prune unnecessary parameters in the learnable prompts can further reduce the memory requirements while maintaining performance.

Model Compression: Employing model compression algorithms like pruning, distillation, or quantization specifically tailored to the learnable prompts can optimize the memory usage and speed up optimization time.

By implementing these optimizations, the learnable prompts can be made more memory-efficient and optimized for real-time deployment in practical applications.