Task Residual for Tuning Vision-Language Models (2211.10277v2)

Abstract: Large-scale vision-LLMs (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes.

• The paper introduces TaskRes, a method that preserves pre-trained classifier weights while incorporating independent task residuals for targeted learning.
• The approach outperforms existing efficient transfer learning methods on 11 diverse benchmarks, surpassing zero-shot CLIP and adapter tuning in accuracy.
• Its simplicity—a one-line integration—and innovative decoupling strategy offer a scalable framework for efficiently adapting vision-language models to new tasks.

Task Residual for Tuning Vision-LLMs: An Expert Overview

The paper the essay is based on addresses the challenges in effectively tuning large-scale vision-LLMs (VLMs) for downstream tasks using a novel concept called Task Residual Tuning (TaskRes). Large-scale VLMs, such as CLIP, have demonstrated exceptional capabilities in learning general visual representations by capturing image-text relationships through massive datasets. However, transferring these capabilities to tasks with limited data remains challenging. Most existing efficient transfer learning (ETL) approaches either inadvertently alter the learned knowledge from pre-trained models or rely excessively on pre-trained features without adequately incorporating task-specific information. TaskRes aims to rectify these challenges by introducing a mechanism that explicitly decouples prior knowledge from task-specific parameters.

The authors present TaskRes as a solution that preserves the integrity of the pre-trained model while allowing for adaptive learning specific to the tasks at hand. The method keeps the original classifier weights unchanged, introducing a set of additional parameters — the task residual — which are independent of prior model knowledge. This task residual serves as an additive component to the pre-existing classifier weights, ensuring that while the base knowledge is retained, the adaptation can explore new, task-specific representations effectively.

The paper details significant performance improvements using TaskRes across multiple benchmark datasets. The experiments covered 11 datasets with varying characteristics, including ImageNet, StanfordCars, and EuroSAT. TaskRes outperforms traditional ETL methods such as prompt tuning and adapter-style tuning, achieving higher accuracy with minimal implementation effort. Notably, it surpasses the capability of Zero-shot CLIP in tasks with limited data, like few-shot learning scenarios.

Some bold claims are associated with the performance of TaskRes, primarily in its ability to achieve a new state-of-the-art across datasets while maintaining computational efficiency. TaskRes requires only a line of code for integration, making it not only effective but also straightforward for practical use in diverse real-world applications.

From a theoretical perspective, the successful implementation of TaskRes prompts further speculations on future developments in AI. The idea of decoupling learning parameters into prior and task-specific domains offers insights into more scalable and adaptable models. Furthermore, its application highlights the feasibility of achieving optimized performance without the need for extensive model re-training or large-scale data availability.

As AI continues to evolve, the implications of tuning mechanisms like TaskRes become impactful in expanding the accessibility of AI technology across various sectors. By reducing dependency on large datasets, this approach may pave the way for more inclusive and diverse AI systems, facilitating broader adoption and innovation. Future work could involve exploring TaskRes in other domains, such as natural language processing or audio-visual tasks, where similar challenges exist.

In conclusion, TaskRes represents a significant advancement in efficiently adapting vision-LLMs for specific tasks. This paper not only contributes to an improved understanding of ETL techniques in the context of VLMs but also provides a practical framework for deploying AI models effectively while maintaining their foundational knowledge. Such strides toward more efficient transfer learning are likely to catalyze further research and development in robust AI systems.