LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts (2410.08211v1)

Abstract: Large-scale vision-language pre-trained (VLP) models (e.g., CLIP) are renowned for their versatility, as they can be applied to diverse applications in a zero-shot setup. However, when these models are used in specific domains, their performance often falls short due to domain gaps or the under-representation of these domains in the training data. While fine-tuning VLP models on custom datasets with human-annotated labels can address this issue, annotating even a small-scale dataset (e.g., 100k samples) can be an expensive endeavor, often requiring expert annotators if the task is complex. To address these challenges, we propose LatteCLIP, an unsupervised method for fine-tuning CLIP models on classification with known class names in custom domains, without relying on human annotations. Our method leverages Large Multimodal Models (LMMs) to generate expressive textual descriptions for both individual images and groups of images. These provide additional contextual information to guide the fine-tuning process in the custom domains. Since LMM-generated descriptions are prone to hallucination or missing details, we introduce a novel strategy to distill only the useful information and stabilize the training. Specifically, we learn rich per-class prototype representations from noisy generated texts and dual pseudo-labels. Our experiments on 10 domain-specific datasets show that LatteCLIP outperforms pre-trained zero-shot methods by an average improvement of +4.74 points in top-1 accuracy and other state-of-the-art unsupervised methods by +3.45 points.

An Expert Analysis of LATTE CLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts

The presented paper, "LATTE CLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts," addresses the challenge of adapting vision-language pre-trained models, such as CLIP, to perform better in specialized domains where domain gaps and under-represented data can significantly hinder their performance. The traditional approach to bridging this performance gap involves supervised fine-tuning, which is costly and requires extensive human-annotated datasets, often including expert involvement. This paper proposes an innovative unsupervised approach, LATTE CLIP, for fine-tuning CLIP models in specialized classification tasks without reliance on human annotations.

Methodological Contributions

LATTE CLIP leverages Large Multimodal Models (LMMs) to generate various levels of expressive textual descriptions for images, enhancing the contextual information available during fine-tuning. A significant advancement comes from the synthesis of three types of descriptions:

• Class-Description: Derived from a typical class template text using pseudo-labels.
• Image-Description: Generated per individual image, capturing its unique characteristics.
• Group-Description: Aggregated from groups of images sharing the same pseudo-label to capture common class attributes.

This multitude of descriptions helps counterbalance the noise and hallucinations typically present in LMM-generated texts.

A novel aspect of LATTE CLIP's approach is its prototype-based fine-tuning method. By representing each class as a set of prototypes (feature vectors), the method maintains control over class representations and facilitates better handling of the synthetic data. These prototypes are dynamically updated using a momentum mechanism, providing stability and robustness throughout the training process. A Dynamic Feature Mixer assigns varying weights to the synthetic textual descriptions, prioritizing more informative and relevant features, which aids in refining per-class prototype representations.

Furthermore, the paper introduces the concept of dual pseudo-labels drawn from both the zero-shot model and the fine-tuned model. This dual approach harnesses the generalization power of the pre-trained model alongside the enhanced target performance from the fine-tuned model, offering an effective balance.

Empirical Validation and Implications

The empirical evaluations demonstrate LATTE CLIP's effectiveness over various unsupervised baselines, including ReCLIP and a FLYP-based adaptation with pseudo-labeling, across ten domain-specific datasets. Notably, LATTE CLIP achieves a consistent improvement in top-1 accuracy by an average of +4.74 points compared to zero-shot CLIP and by +3.45 points over other unsupervised state-of-the-art methods. These results underline LATTE CLIP's potential in unsupervised model adaptation without labeled training data.

The theoretical implications of this research are noteworthy. By decoupling the reliance on human annotation for fine-tuning, LATTE CLIP introduces a practical approach to domain adaptation, providing a cost-effective solution adaptable to various domains. The method's architecture also invites future explorations into more granular or hierarchical structures of synthetic descriptions to further enhance the model's descriptive granularity.

Future Directions

With LLMs and multimodal counterparts advancing rapidly, the augmentation of pre-trained models with sophisticated synthetic data is poised to redefine unsupervised adaptation landscapes. Future work can explore improving description generation quality, leveraging more advanced LMM architectures, and evaluating the broader generalization capabilities across more diverse and nuanced domains.

In conclusion, while LATTE CLIP does not claim breakthrough status, it indeed sets a pragmatic path for unsupervised fine-tuning methodologies, merging the expressive capabilities of LMMs with structured prototype-based learning, marking a noteworthy step in the evolving field of vision-LLMs.