Soft Context Sharing for Prompt Tuning Improves Performance of Vision-Language Models on Multiple Few-Shot Tasks

Keskeiset käsitteet

Soft sharing of prompt context across multiple few-shot tasks can significantly improve the performance of vision-language models compared to single-task prompt tuning.

Tiivistelmä

This paper proposes a novel method called SoftCPT (Soft Context Sharing for Prompt Tuning) to tune pre-trained vision-language models on multiple target few-shot tasks jointly.
The key idea is to design a task-shared meta network that generates prompt context for each task using the task name and a learnable task context as input. The parameters of this meta network as well as the task context are tuned on the joint training set of all tasks. This allows the prompt context of all tasks to be shared in a soft manner, enabling effective knowledge transfer across related tasks.
Extensive experiments are conducted on four multi-task few-shot datasets covering 44 tasks and 1593 categories. The results show that SoftCPT significantly outperforms single-task prompt tuning methods, highlighting the effectiveness of multi-task learning for vision-language prompt tuning.
The paper also constructs a new few-shot fashion classification dataset to test the effectiveness of multi-task prompt tuning in a real industrial scenario. Ablation studies are performed to analyze the key components of SoftCPT, such as the necessity of adding class features to task features and the impact of different sub-network structures.
Overall, the proposed SoftCPT method demonstrates the benefits of soft prompt sharing across multiple related tasks for improving the performance of vision-language models on few-shot recognition.

Tilastot

Vision-language models have recently shown great potential on many computer vision tasks.
Prior work demonstrates prompt tuning can acquire superior performance on few-shot image recognition compared to linear probe.
Many few-shot tasks are inherently correlated, particularly within specialized domains, but this information was overlooked previously.

Lainaukset

"Prompt Tuning with Soft Context Sharing for Vision-Language Models"
"Extensive experiments across four multi-task few-shot datasets covering 44 tasks and 1593 categories demonstrate that SoftCPT significantly outperforms single-task prompt tuning methods, highlighting the effectiveness of multi-task learning for vision-language prompt tuning."

Tärkeimmät oivallukset

Prompt Tuning with Soft Context Sharing for Vision-Language Models

by Kun Ding,Yin... klo arxiv.org 04-02-2024

https://arxiv.org/pdf/2208.13474.pdf

Syvällisempiä Kysymyksiä

Can the proposed soft prompt sharing approach be extended to other parameter-efficient fine-tuning methods beyond prompt tuning, such as adapter-based methods

The proposed soft prompt sharing approach can potentially be extended to other parameter-efficient fine-tuning methods beyond prompt tuning, such as adapter-based methods. Adapter-based methods involve inserting or attaching tuneable layers to pre-trained models, allowing for task-specific modifications without altering the entire model. In the context of soft prompt sharing, the learnable task context generated by the meta network could be integrated as an adapter layer, enabling task-specific adjustments while maintaining the efficiency of parameter-efficient fine-tuning. By incorporating the soft prompt sharing mechanism into adapter-based methods, it is possible to enhance the adaptability and performance of vision-language models across various downstream tasks.

How would the performance of SoftCPT be affected if the task relationships are not well-captured by the text encoder, for example, in the case of highly specialized tasks with obscure task names

If the task relationships are not well-captured by the text encoder, especially in the case of highly specialized tasks with obscure task names, the performance of SoftCPT may be impacted. The effectiveness of SoftCPT relies on the ability of the text encoder to extract meaningful task features from task names, which are essential for generating task-specific prompt contexts. In scenarios where task relationships are not clearly reflected in the task names or where the text encoder struggles to capture the nuances of highly specialized tasks, the generated prompt contexts may not accurately represent the underlying task similarities. This could lead to suboptimal performance in multi-task prompt tuning, as the shared prompt contexts may not effectively capture the task-relatedness required for knowledge transfer across tasks.

What other techniques beyond multi-task learning could be explored to further improve the generalization ability of vision-language prompt tuning

Beyond multi-task learning, several other techniques could be explored to further improve the generalization ability of vision-language prompt tuning. Some potential approaches include:

Meta-Learning: Meta-learning techniques, such as model-agnostic meta-learning (MAML) or Reptile, could be applied to adapt the pre-trained vision-language model to new tasks with limited data. By meta-learning the prompt tuning process, the model can quickly adapt to new tasks and generalize better across a wide range of tasks.

Data Augmentation: Leveraging data augmentation techniques specific to vision and language modalities can help improve the model's ability to generalize to unseen data. By augmenting the training data with diverse transformations and perturbations, the model can learn more robust and generalizable representations.

Regularization Techniques: Incorporating regularization methods like dropout, weight decay, or batch normalization can help prevent overfitting and improve the model's generalization performance. Regularization techniques can enhance the model's ability to generalize well to new tasks by reducing the risk of memorizing noise in the training data.

Domain Adaptation: Utilizing domain adaptation techniques to align the distributions of different tasks or domains can improve the model's generalization across diverse datasets. By adapting the model to the specific characteristics of each task or domain, the model can better transfer knowledge and perform well on new tasks.

Exploring these additional techniques in conjunction with multi-task learning can further enhance the generalization ability of vision-language prompt tuning models and improve their performance across a wide range of tasks and domains.