External Knowledge Injection for CLIP-Based Class-Incremental Learning

• URL: http://arxiv.org/abs/2503.08510v1
• Date: Tue, 11 Mar 2025 15:00:22 GMT
• Title: External Knowledge Injection for CLIP-Based Class-Incremental Learning
• Authors: Da-Wei Zhou, Kai-Wen Li, Jingyi Ning, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan,
• Abstract summary: Class-Incremental Learning (CIL) enables learning systems to continuously adapt to evolving data streams.<n>We introduce ExterNal knowledGe INjEction (ENGINE) for CLIP-based CIL.
• Score: 62.516402566610395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-Incremental Learning (CIL) enables learning systems to continuously adapt to evolving data streams. With the advancement of pre-training, leveraging pre-trained vision-language models (e.g., CLIP) offers a promising starting point for CIL. However, CLIP makes decisions by matching visual embeddings to class names, overlooking the rich contextual information conveyed through language. For instance, the concept of ``cat'' can be decomposed into features like tail, fur, and face for recognition. Besides, since the model is continually updated, these detailed features are overwritten in CIL, requiring external knowledge for compensation. In this paper, we introduce ExterNal knowledGe INjEction (ENGINE) for CLIP-based CIL. To enhance knowledge transfer from outside the dataset, we propose a dual-branch injection tuning framework that encodes informative knowledge from both visual and textual modalities. The visual branch is enhanced with data augmentation to enrich the visual features, while the textual branch leverages GPT-4 to rewrite discriminative descriptors. In addition to this on-the-fly knowledge injection, we also implement post-tuning knowledge by re-ranking the prediction results during inference. With the injected knowledge, the model can better capture informative features for downstream tasks as data evolves. Extensive experiments demonstrate the state-of-the-art performance of ENGINE. Code is available at: https://github.com/RenaissCode/ENGINE

Related papers

• Adapter-Enhanced Semantic Prompting for Continual Learning [91.63494614012362]
  Continual learning (CL) enables models to adapt to evolving data streams.<n>Traditional methods usually retain the past data for replay or add additional branches in the model to learn new knowledge.<n>We propose a novel lightweight CL framework, which integrates prompt tuning and adapter techniques.
  arXiv  Detail & Related papers  (2024-12-15T06:14:55Z)
• Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning [70.64617500380287]
  Continual learning allows models to learn from new data while retaining previously learned knowledge. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. We propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings.
  arXiv  Detail & Related papers  (2024-08-02T07:51:44Z)
• Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
  We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models. Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
  arXiv  Detail & Related papers  (2023-08-22T04:24:45Z)
• AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning [53.32576252950481]
  Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. In this paper, we propose a non-incremental learner, named AttriCLIP, to incrementally extract knowledge of new classes or tasks.
  arXiv  Detail & Related papers  (2023-05-19T07:39:17Z)
• HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models [30.279621764192843]
  Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors. We propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization.
  arXiv  Detail & Related papers  (2023-03-28T07:54:54Z)
• Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners [55.119101947682715]
  We propose CaFo, a Cascade of Foundation models that incorporates diverse prior knowledge of various pre-training paradigms for better few-shot learning. Our CaFo incorporates CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, DALL-E's vision-generative knowledge, and GPT-3's language-generative knowledge.
  arXiv  Detail & Related papers  (2023-03-03T18:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.