Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery

• URL: http://arxiv.org/abs/2403.07369v1
• Date: Tue, 12 Mar 2024 07:06:50 GMT
• Title: Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery
• Authors: Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong
• Abstract summary: Generalized Category Discovery (GCD) aims to cluster unlabeled data from both known and unknown categories. Current GCD methods rely on only visual cues, which neglect the multi-modality perceptive nature of human cognitive processes in discovering novel visual categories. We propose a two-phase TextGCD framework to accomplish multi-modality GCD by exploiting powerful Visual-Language Models.
• Score: 69.91441987063307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labeled data from known categories. Current GCD methods rely on only visual cues, which however neglect the multi-modality perceptive nature of human cognitive processes in discovering novel visual categories. To address this, we propose a two-phase TextGCD framework to accomplish multi-modality GCD by exploiting powerful Visual-Language Models. TextGCD mainly includes a retrieval-based text generation (RTG) phase and a cross-modality co-teaching (CCT) phase. First, RTG constructs a visual lexicon using category tags from diverse datasets and attributes from Large Language Models, generating descriptive texts for images in a retrieval manner. Second, CCT leverages disparities between textual and visual modalities to foster mutual learning, thereby enhancing visual GCD. In addition, we design an adaptive class aligning strategy to ensure the alignment of category perceptions between modalities as well as a soft-voting mechanism to integrate multi-modality cues. Experiments on eight datasets show the large superiority of our approach over state-of-the-art methods. Notably, our approach outperforms the best competitor, by 7.7% and 10.8% in All accuracy on ImageNet-1k and CUB, respectively.

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