SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models (2210.03794v1)

Abstract: Vision-LLMs such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available. In this work, we show that while effective on internet-style datasets, even those remedies under-deliver on classification tasks with images that differ significantly from those commonly found online. To address this issue, we present a new approach called SVL-Adapter that combines the complementary strengths of both vision-language pretraining and self-supervised representation learning. We report an average classification accuracy improvement of 10% in the low-shot setting when compared to existing methods, on a set of challenging visual classification tasks. Further, we present a fully automatic way of selecting an important blending hyperparameter for our model that does not require any held-out labeled validation data. Code for our project is available here: https://github.com/omipan/svl_adapter.

SVL-Adapter: A Novel Approach to Enhancing Vision-LLMs Using Self-Supervised Learning

The paper "SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models" introduces an innovative method to adapt vision-LLMs such as CLIP to domains with visual properties substantially different from those reflected in standard internet-sourced datasets. The proposed SVL-Adapter harnesses the dual strengths of self-supervised learning (SSL) and vision-language pretraining, delivering key improvements in low-shot settings across the paper’s challenging visual classification tasks.

Large-scale vision-LLMs like CLIP bring notable capabilities in zero- and low-shot learning by leveraging representations obtained from vast internet-based image-text pairs. However, their adaptation to new tasks, especially those outside typical image distributions seen online, remains computationally costly and data-dependent. Current methods attempt lightweight model adaptations, such as prompt learning and feature adaptation, yet these fall short when substantial domain shifts occur, such as those seen in medical or remote sensing imagery.

SVL-Adapter proposes a more robust strategy. It combines CLIP’s powerful zero-shot classifiers with self-supervised representations specifically tuned for target domains. It reports an average improvement in classification accuracy by 10% in low-shot settings over standard methods. Furthermore, it introduces a technique for automatic hyperparameter selection, which does not require labeled validation data, leveraging CLIP’s confidence levels.

The methodology involves constructing a self-supervised encoder trained on domain-specific data, which supplements the encoded representations obtained from CLIP. This fusion occurs at the class prediction stage using a blending weight $\lambda$. Importantly, this parameter can be set automatically based on CLIP’s average confidence across test images, thus enabling more effective adaptation without labeled validation data.

A notable contribution of the paper is the structural and parametric simplicity of SVL-Adapter relative to existing alternatives while maintaining competitive results in more conventional datasets. It maintains a strong edge on datasets where image characteristics vary dramatically from those of conventional internet-style datasets.

Extensive experimentation underlines these findings. The SVL-Adapter consistently outperformed baseline models like CoOp, CLIP-Adapter, and Tip-Adapter across a range of datasets, including those of satellite imagery and biodiversity monitoring. These findings underscore the utility of SVL-Adapter in real-world applications where images are abundant, but labels are scarce or challenging to obtain.

Theoretical implications derived from SVL-Adapter suggest a promising direction for the integration of vision-LLMs with SSL techniques, particularly for tasks that deviate from traditional internet image distributions. Practically, its approach of utilizing self-supervised learning to enhance domain adaptation offers a tangible pathway for improving model performance in more diverse and challenging visual environments—it enhances the adaptability and generality of pretrained models without incurring significant additional labeling costs.

Speculatively, future developments may explore integrating more advanced self-supervised methods, potentially learning embeddings that capture even richer semantic features. The ongoing expansion of vision-language architectures will likely find further hybridization with SSL paradigms as researchers seek more adaptive, less resource-intensive solutions.

In sum, the SVL-Adapter presents an important contribution to vision-language learning, underlining the value of self-supervised learning to adapt established models to non-cohesive visual tasks, all while limiting the extent of required computational and labeled resources.