Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification

• URL: http://arxiv.org/abs/2408.08125v1
• Date: Thu, 15 Aug 2024 12:51:57 GMT
• Title: Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
• Authors: Jiexuan Yan, Sheng Huang, Nankun Mu, Luwen Huangfu, Bo Liu,
• Abstract summary: Category-Prompt Refined Feature Learning (CPRFL) is a novel approach for Long-Tailed Multi-Label image Classification. CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over baselines.
• Score: 8.139529179222844
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Multi-Label image Classification (LTMLC). In such contexts, imbalanced data distribution and multi-object recognition pose significant hurdles. To address this issue, we propose a novel and effective approach for LTMLC, termed Category-Prompt Refined Feature Learning (CPRFL), utilizing semantic correlations between different categories and decoupling category-specific visual representations for each category. Specifically, CPRFL initializes category-prompts from the pretrained CLIP's embeddings and decouples category-specific visual representations through interaction with visual features, thereby facilitating the establishment of semantic correlations between the head and tail classes. To mitigate the visual-semantic domain bias, we design a progressive Dual-Path Back-Propagation mechanism to refine the prompts by progressively incorporating context-related visual information into prompts. Simultaneously, the refinement process facilitates the progressive purification of the category-specific visual representations under the guidance of the refined prompts. Furthermore, taking into account the negative-positive sample imbalance, we adopt the Asymmetric Loss as our optimization objective to suppress negative samples across all classes and potentially enhance the head-to-tail recognition performance. We validate the effectiveness of our method on two LTMLC benchmarks and extensive experiments demonstrate the superiority of our work over baselines. The code is available at https://github.com/jiexuanyan/CPRFL.

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