Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition

TL;DR

This paper introduces Sage, a semantic-guided fine-tuning method for foundation models that improves long-tailed visual recognition by aligning visual and textual modalities and addressing distribution mismatch bias.

Contribution

The paper proposes a novel SG-Adapter and a distribution mismatch-aware compensation factor to enhance semantic alignment and rectify bias in long-tailed visual recognition.

Findings

Sage significantly improves performance on long-tailed datasets.

Semantic guidance enhances visual-textual alignment.

The compensation factor effectively reduces prediction bias.

Abstract

The variance in class-wise sample sizes within long-tailed scenarios often results in degraded performance in less frequent classes. Fortunately, foundation models, pre-trained on vast open-world datasets, demonstrate strong potential for this task due to their generalizable representation, which promotes the development of adaptive strategies on pre-trained models in long-tailed learning. Advanced fine-tuning methods typically adjust visual encoders while neglecting the semantics derived from the frozen text encoder, overlooking the visual and textual alignment. To strengthen this alignment, we propose a novel approach, Semantic-guided fine-tuning of foundation model for long-tailed visual recognition (Sage), which incorporates semantic guidance derived from textual modality into the visual fine-tuning process. Specifically, we introduce an SG-Adapter that integrates class descriptions as semantic guidance to guide the fine-tuning of the visual encoder. The introduced guidance is passesed through the attention mechanism and enables the model to focus more on semantically relevant content, strengthening the alignment between the visual and textual modalities. Due to the inconsistent class-conditional distributions neglected by the existing loss function, the resulting prediction bias causes performance improvements for the tail class less than for the head class, even when the multi-modal alignment is enhanced. To address this challenge, we propose a novel distribution mismatch-aware compensation factor, which is specifically designed to rectify the prediction bias caused by the ignored inconsistent distribution based on our theoretical analysis, and is seamlessly integrated into the loss function. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed Sage in enhancing performance in long-tailed learning.