External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection

Core Concepts

A novel parameter-efficient fine-tuning method, ExPert, that enhances the salient object detection capability of pre-trained transformer models by incorporating external prompt features.

Abstract

The paper proposes the ExPert model, a novel parameter-efficient fine-tuning method for salient object detection (SOD) using pre-trained transformer backbones.
The key aspects of ExPert are:

Encoder-Decoder Architecture:

ExPert uses a multi-scale transformer encoder (SegFormer) and a decoder for SOD.
The encoder backbone is frozen during training to reduce the number of trained parameters.

E-Adapter Module:

ExPert employs a block-level adapter module (E-adapter) to fine-tune the pre-trained transformer backbone in a parameter-efficient manner.
The E-adapter reduces the feature dimensionality through a bottleneck design, thereby diminishing the number of trained parameters.

E-Injector Module:

ExPert introduces an E-injector module to inject external prompt features from other pre-trained models (DINO, ViT, BLIP) into the frozen encoder backbone.
The injected prompt features enhance the awareness of salient objects in the backbone.
Experiments show that combining ViT and BLIP's interacted features achieves the best performance.

Comprehensive Experiments:

ExPert surpasses state-of-the-art CNN-based and transformer-based models across five SOD datasets.
ExPert achieves 0.215 mean absolute error (MAE) on the ECSSD dataset with only 80.2M trained parameters, which is 21% better than the transformer-based SOTA model and 47% better than the CNN-based SOTA model.

The proposed ExPert model demonstrates the effectiveness of parameter-efficient fine-tuning with external prompt features for enhancing the salient object detection capability of pre-trained transformer backbones.

Stats

ExPert achieves 0.215 mean absolute error (MAE) on the ECSSD dataset, which is 21% better than the transformer-based SOTA model and 47% better than the CNN-based SOTA model.
ExPert has only 80.2M trained parameters, which is more parameter-efficient than all SOTA models except EVP.

Quotes

"Comprehensive experiments demonstrate the superiority of our method. Surpassing former state-of-the-art (SOTA) models across five SOD datasets, ExPert achieves 0.215 mean absolute error (MAE) in ECSSD dataset with 80.2M trained parameters, 21% better than transformer-based SOTA model and 47% better than CNN-based SOTA model."

Key Insights Distilled From

External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection

by Wen Liang,Pe... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15008.pdf

Deeper Inquiries

How can the external prompt features be further enhanced or diversified to improve the salient object detection performance of ExPert

To further enhance the external prompt features and improve the salient object detection performance of ExPert, several strategies can be considered:

Semantic Segmentation Features: Incorporating features from models specifically trained for semantic segmentation tasks can provide more detailed information about object boundaries and shapes, enhancing the model's ability to accurately detect salient objects.

Texture and Color Information: Including prompt features that focus on texture and color information can help the model differentiate between objects with similar shapes but distinct textures or colors, improving the segmentation accuracy in complex scenes.

Temporal Information: Introducing prompt features that capture temporal information can aid in detecting moving or dynamic objects in videos, expanding the model's capabilities beyond static images.

Multi-Modal Features: Combining visual prompts with other modalities like depth information or infrared imaging can offer a more comprehensive understanding of the scene, leading to more robust salient object detection in diverse environments.

Attention Mechanisms: Implementing attention mechanisms to dynamically adjust the importance of different prompt features based on the context of the image can further refine the model's focus on salient regions.

By diversifying and enhancing the external prompt features with these strategies, ExPert can achieve even higher performance in salient object detection tasks.

What other computer vision tasks, beyond salient object detection, could benefit from the parameter-efficient fine-tuning approach used in ExPert

The parameter-efficient fine-tuning approach used in ExPert can benefit various other computer vision tasks beyond salient object detection, including:

Semantic Segmentation: By adapting the model to segment different classes of objects in an image, the fine-tuning method can improve the accuracy and efficiency of semantic segmentation tasks.

Instance Segmentation: Fine-tuning large models for instance segmentation, where the goal is to detect and segment individual objects within an image, can lead to more precise object delineation and classification.

Object Detection: The parameter-efficient fine-tuning technique can enhance object detection models by refining the localization and classification of objects in images, especially in scenarios with multiple overlapping objects.

Image Classification: Adapting pre-trained models for image classification tasks can improve the model's ability to classify images into various categories with fewer training parameters.

Video Analysis: Extending the approach to video analysis tasks such as action recognition or object tracking can optimize the model for processing sequential data efficiently.

By applying the parameter-efficient fine-tuning approach to these tasks, models can achieve superior performance while minimizing the computational resources required for training.

Can the ideas behind ExPert be extended to fine-tune large language models for natural language processing tasks in a more parameter-efficient manner

The concepts behind ExPert can indeed be extended to fine-tune large language models for natural language processing (NLP) tasks in a more parameter-efficient manner. Here's how this extension can be implemented:

Prompt-based Fine-tuning: Similar to how ExPert uses external prompt features to guide the model in salient object detection, prompt-based fine-tuning can be applied to language models. By providing specific prompts related to the NLP task at hand, the model can adapt more efficiently to new tasks without extensive retraining.

Adapter Modules: Utilizing adapter modules, as seen in ExPert, can help fine-tune large language models with fewer parameters. These adapters can selectively adjust specific parts of the model for different NLP tasks, enhancing performance while maintaining parameter efficiency.

Multi-Modal Inputs: Integrating prompt features from multiple modalities, such as text and images, can enable the model to handle diverse NLP tasks that involve multi-modal inputs, such as image captioning or visual question answering.

Attention Mechanisms: Incorporating attention mechanisms to focus on relevant parts of the input data can improve the model's understanding of complex language structures and relationships, leading to more accurate NLP predictions.

By extending the ideas behind ExPert to fine-tune large language models for NLP tasks, researchers can develop more efficient and adaptable models that excel in a wide range of natural language understanding tasks.