Einblick - Computer Vision - # Transferable Visual Prompting for Multimodal Large Language Models

Enhancing the Performance of Diverse Multimodal Large Language Models through Transferable Visual Prompting

Q: How can the transferability of visual prompts be further improved to benefit an even broader range of Multimodal Large Language Models?

To enhance the transferability of visual prompts across a broader range of Multimodal Large Language Models (MLLMs), several strategies can be considered: Diverse Prompt Training: Training visual prompts on a more diverse set of MLLMs can help capture a wider range of model-specific features and representations. By exposing the prompts to a variety of model architectures and pre-training data, they can learn to adapt more effectively to different models. Transfer Learning Techniques: Leveraging transfer learning techniques can improve the generalization of visual prompts. By fine-tuning prompts on a larger dataset or pre-trained models and then transferring them to target MLLMs, the prompts can capture more universal features that benefit a broader range of models. Regularization Methods: Introducing regularization techniques during prompt training can help prevent overfitting to specific models and encourage the prompts to capture more generic features. Regularization can promote the prompts' ability to transfer knowledge across different MLLMs. Task-Specific Adaptation: Tailoring the prompts to specific downstream tasks can enhance their transferability. By incorporating task-specific information during prompt training, the prompts can better align with the requirements of diverse tasks and models. Ensemble Learning: Utilizing ensemble methods with prompts trained on multiple models can improve transferability. By combining prompts from different models, the ensemble can capture a more comprehensive set of features and enhance performance across a broader range of MLLMs. By implementing these strategies, the transferability of visual prompts can be further improved to benefit a wider array of Multimodal Large Language Models, enabling more efficient adaptation to diverse downstream tasks.

Q: What are the potential limitations or drawbacks of the proposed Transferable Visual Prompting approach, and how can they be addressed?

While Transferable Visual Prompting (TVP) offers a promising solution for enhancing the performance of Multimodal Large Language Models (MLLMs), there are potential limitations and drawbacks that need to be addressed: Overfitting to Specific Models: One limitation of TVP is the risk of prompts overfitting to the model on which they are trained, leading to reduced transferability to other models. To address this, regularization techniques can be employed to prevent prompts from capturing model-specific features excessively. Task-Specificity: TVP may focus too much on task-specific features during prompt training, limiting its adaptability to a broader range of tasks. Balancing task-agnostic knowledge with task-specific information can help improve the transferability of prompts across diverse tasks. Prompt Size and Complexity: The size and complexity of visual prompts can impact their transferability. Large or intricate prompts may be less effective in transferring knowledge to different models. Optimizing prompt size and complexity based on the target models can mitigate this limitation. Data Distribution Discrepancies: TVP may struggle with transferring prompts across models trained on significantly different data distributions. Addressing this limitation requires strategies to align the data distributions or adapt prompts to diverse data sources effectively. Computational Resources: Training visual prompts for multiple models can be computationally intensive. Implementing efficient training procedures, such as parallel processing or distributed computing, can help mitigate the resource requirements of TVP. By addressing these limitations through careful design of the training process, regularization techniques, and model-agnostic feature extraction, the effectiveness and applicability of TVP can be enhanced for a broader range of MLLMs and downstream tasks.

Q: What other types of shared parameters, beyond visual prompts, could be explored to enhance the performance of diverse Multimodal Large Language Models in a resource-efficient manner?

In addition to visual prompts, several other types of shared parameters can be explored to enhance the performance of diverse Multimodal Large Language Models (MLLMs) in a resource-efficient manner: Textual Prompts: Similar to visual prompts, textual prompts can be used to guide MLLMs in processing multimodal inputs. By providing task-specific textual cues, MLLMs can better align visual and textual information for improved performance on downstream tasks. Adaptive Adapters: Adaptive adapters are lightweight modules that can be added to pre-trained models to adapt them to specific tasks. By fine-tuning these adapters on a target task and sharing them across multiple models, the performance of diverse MLLMs can be enhanced efficiently. Knowledge Distillation: Knowledge distillation involves transferring knowledge from a large, complex model to a smaller, more efficient model. By distilling the knowledge learned by a specialized MLLM into a simpler model, the performance of the latter can be improved without the need for extensive training. Task-Specific Embeddings: Task-specific embeddings can be shared across multiple MLLMs to provide a common representation space for different models. By aligning the embeddings with task-specific features, MLLMs can leverage shared knowledge for enhanced performance on various tasks. Attention Mechanisms: Shared attention mechanisms can help MLLMs focus on relevant parts of the input data for different tasks. By incorporating attention mechanisms that are optimized for specific tasks and shared across models, the models can improve their performance efficiently. Exploring these shared parameters in conjunction with visual prompts can offer a comprehensive approach to enhancing the performance of diverse Multimodal Large Language Models in a resource-efficient manner. By leveraging a combination of different parameter types, MLLMs can adapt effectively to various tasks and datasets while minimizing computational overhead.

Kernkonzepte

Transferable Visual Prompting (TVP) can effectively improve the performance of diverse Multimodal Large Language Models (MLLMs) on a wide range of tasks by optimizing a set of shared visual prompts.

Zusammenfassung

The paper explores a novel setting where the goal is to improve the performance of diverse MLLMs on a specific downstream task by optimizing a set of shared parameters, rather than fine-tuning each model independently.

The key insights are:

Existing visual prompting methods can effectively enhance the performance of the model used for prompt training, but the learned prompts often fail to transfer well to other MLLMs due to "cross-model feature corruption".
To address this issue, the authors propose Transferable Visual Prompting (TVP), which integrates two novel strategies:
- Feature Consistency Alignment (FCA): Imposes constraints on the prompted features to maintain task-agnostic knowledge and prevent excessive feature changes.
- Task Semantics Enrichment (TSE): Leverages CLIP to explicitly embed task-specific semantics into the visual prompts.
Extensive experiments on 10 diverse datasets covering visual recognition, counting, reasoning, and hallucination tasks demonstrate that TVP can effectively boost the performance of 6 modern MLLMs, significantly outperforming existing visual prompting baselines.
TVP exhibits good generalization across datasets and robustness to image corruptions, highlighting its practicality in real-world scenarios.
Compared to fine-tuning methods, TVP provides a more resource-friendly and flexible solution to adapt different models for a given task simultaneously.

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Statistiken

The visual prompts trained on MiniGPT-4 can improve the top-1 accuracy of InstructBLIP on CIFAR-10 by 36.4%.
The visual prompts trained on InstructBLIP can improve the top-1 accuracy of BLIVA on ImageNette by 25.4%.
The visual prompts trained on the ensemble of MiniGPT-4 and InstructBLIP can improve the top-1 accuracy of BLIP2 on SVHN by 28.8%.
The visual prompts trained on the ensemble of MiniGPT-4 and InstructBLIP can improve the AUC score of VPGTrans on Hatefulmemes by 26.9%.

Zitate

"The visual prompts trained with one single model can facilitate the overall performance of 6 modern MLLMs on 10 datasets ranging from visual tasks like recognition and counting to multimodal reasoning and hallucination correction, which significantly surpasses the existing visual prompting baselines."
"TVP can enhance different models with diverse data scales, generalize to different datasets, and resist image corruptions, emphasizing the practicality of our method in real scenarios."

Wichtige Erkenntnisse aus

Exploring the Transferability of Visual Prompting for Multimodal Large Language Models

by Yichi Zhang,... um arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11207.pdf

Exploring the Transferability of Visual Prompting for Multimodal Large Language Models

Tiefere Fragen

How can the transferability of visual prompts be further improved to benefit an even broader range of Multimodal Large Language Models?

To enhance the transferability of visual prompts across a broader range of Multimodal Large Language Models (MLLMs), several strategies can be considered:

Diverse Prompt Training: Training visual prompts on a more diverse set of MLLMs can help capture a wider range of model-specific features and representations. By exposing the prompts to a variety of model architectures and pre-training data, they can learn to adapt more effectively to different models.

Transfer Learning Techniques: Leveraging transfer learning techniques can improve the generalization of visual prompts. By fine-tuning prompts on a larger dataset or pre-trained models and then transferring them to target MLLMs, the prompts can capture more universal features that benefit a broader range of models.

Regularization Methods: Introducing regularization techniques during prompt training can help prevent overfitting to specific models and encourage the prompts to capture more generic features. Regularization can promote the prompts' ability to transfer knowledge across different MLLMs.

Task-Specific Adaptation: Tailoring the prompts to specific downstream tasks can enhance their transferability. By incorporating task-specific information during prompt training, the prompts can better align with the requirements of diverse tasks and models.

Ensemble Learning: Utilizing ensemble methods with prompts trained on multiple models can improve transferability. By combining prompts from different models, the ensemble can capture a more comprehensive set of features and enhance performance across a broader range of MLLMs.

By implementing these strategies, the transferability of visual prompts can be further improved to benefit a wider array of Multimodal Large Language Models, enabling more efficient adaptation to diverse downstream tasks.

What are the potential limitations or drawbacks of the proposed Transferable Visual Prompting approach, and how can they be addressed?

While Transferable Visual Prompting (TVP) offers a promising solution for enhancing the performance of Multimodal Large Language Models (MLLMs), there are potential limitations and drawbacks that need to be addressed:

Overfitting to Specific Models: One limitation of TVP is the risk of prompts overfitting to the model on which they are trained, leading to reduced transferability to other models. To address this, regularization techniques can be employed to prevent prompts from capturing model-specific features excessively.

Task-Specificity: TVP may focus too much on task-specific features during prompt training, limiting its adaptability to a broader range of tasks. Balancing task-agnostic knowledge with task-specific information can help improve the transferability of prompts across diverse tasks.

Prompt Size and Complexity: The size and complexity of visual prompts can impact their transferability. Large or intricate prompts may be less effective in transferring knowledge to different models. Optimizing prompt size and complexity based on the target models can mitigate this limitation.

Data Distribution Discrepancies: TVP may struggle with transferring prompts across models trained on significantly different data distributions. Addressing this limitation requires strategies to align the data distributions or adapt prompts to diverse data sources effectively.

Computational Resources: Training visual prompts for multiple models can be computationally intensive. Implementing efficient training procedures, such as parallel processing or distributed computing, can help mitigate the resource requirements of TVP.

By addressing these limitations through careful design of the training process, regularization techniques, and model-agnostic feature extraction, the effectiveness and applicability of TVP can be enhanced for a broader range of MLLMs and downstream tasks.

What other types of shared parameters, beyond visual prompts, could be explored to enhance the performance of diverse Multimodal Large Language Models in a resource-efficient manner?

In addition to visual prompts, several other types of shared parameters can be explored to enhance the performance of diverse Multimodal Large Language Models (MLLMs) in a resource-efficient manner:

Textual Prompts: Similar to visual prompts, textual prompts can be used to guide MLLMs in processing multimodal inputs. By providing task-specific textual cues, MLLMs can better align visual and textual information for improved performance on downstream tasks.

Adaptive Adapters: Adaptive adapters are lightweight modules that can be added to pre-trained models to adapt them to specific tasks. By fine-tuning these adapters on a target task and sharing them across multiple models, the performance of diverse MLLMs can be enhanced efficiently.

Knowledge Distillation: Knowledge distillation involves transferring knowledge from a large, complex model to a smaller, more efficient model. By distilling the knowledge learned by a specialized MLLM into a simpler model, the performance of the latter can be improved without the need for extensive training.

Task-Specific Embeddings: Task-specific embeddings can be shared across multiple MLLMs to provide a common representation space for different models. By aligning the embeddings with task-specific features, MLLMs can leverage shared knowledge for enhanced performance on various tasks.

Attention Mechanisms: Shared attention mechanisms can help MLLMs focus on relevant parts of the input data for different tasks. By incorporating attention mechanisms that are optimized for specific tasks and shared across models, the models can improve their performance efficiently.

Exploring these shared parameters in conjunction with visual prompts can offer a comprehensive approach to enhancing the performance of diverse Multimodal Large Language Models in a resource-efficient manner. By leveraging a combination of different parameter types, MLLMs can adapt effectively to various tasks and datasets while minimizing computational overhead.