GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery

TL;DR

This paper introduces a novel multi-modal approach using CLIP's visual and text features for generalized category discovery, significantly improving classification accuracy by synthesizing pseudo text embeddings and fusing modalities.

Contribution

It proposes a Text Embedding Synthesizer (TES) to generate pseudo text embeddings and a dual-branch framework for joint visual-text learning, unlocking CLIP's multi-modal potential for GCD.

Findings

Outperforms baseline methods on all GCD benchmarks

Achieves new state-of-the-art results

Effectively fuses visual and text modalities for better classification

Abstract

Given unlabelled datasets containing both old and new categories, generalized category discovery (GCD) aims to accurately discover new classes while correctly classifying old classes. Current GCD methods only use a single visual modality of information, resulting in a poor classification of visually similar classes. As a different modality, text information can provide complementary discriminative information, which motivates us to introduce it into the GCD task. However, the lack of class names for unlabelled data makes it impractical to utilize text information. To tackle this challenging problem, in this paper, we propose a Text Embedding Synthesizer (TES) to generate pseudo text embeddings for unlabelled samples. Specifically, our TES leverages the property that CLIP can generate aligned vision-language features, converting visual embeddings into tokens of the CLIP's text encoder to generate pseudo text embeddings. Besides, we employ a dual-branch framework, through the joint learning and instance consistency of different modality branches, visual and semantic information mutually enhance each other, promoting the interaction and fusion of visual and text knowledge. Our method unlocks the multi-modal potentials of CLIP and outperforms the baseline methods by a large margin on all GCD benchmarks, achieving new state-of-the-art. Our code is available at: https://github.com/enguangW/GET.