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CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning (2403.10245v1)

Published 15 Mar 2024 in cs.CV

Abstract: This paper explores the problem of continual learning (CL) of vision-LLMs (VLMs) in open domains, where the models need to perform continual updating and inference on a streaming of datasets from diverse seen and unseen domains with novel classes. Such a capability is crucial for various applications in open environments, e.g., AI assistants, autonomous driving systems, and robotics. Current CL studies mostly focus on closed-set scenarios in a single domain with known classes. Large pre-trained VLMs like CLIP have demonstrated superior zero-shot recognition ability, and a number of recent studies leverage this ability to mitigate catastrophic forgetting in CL, but they focus on closed-set CL in a single domain dataset. Open-domain CL of large VLMs is significantly more challenging due to 1) large class correlations and domain gaps across the datasets and 2) the forgetting of zero-shot knowledge in the pre-trained VLMs in addition to the knowledge learned from the newly adapted datasets. In this work we introduce a novel approach, termed CoLeCLIP, that learns an open-domain CL model based on CLIP. It addresses these challenges by a joint learning of a set of task prompts and a cross-domain class vocabulary. Extensive experiments on 11 domain datasets show that CoLeCLIP outperforms state-of-the-art methods for open-domain CL under both task- and class-incremental learning settings.

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Authors (9)
  1. Yukun Li (34 papers)
  2. Guansong Pang (82 papers)
  3. Wei Suo (12 papers)
  4. Chenchen Jing (10 papers)
  5. Yuling Xi (2 papers)
  6. Lingqiao Liu (113 papers)
  7. Hao Chen (1005 papers)
  8. Guoqiang Liang (22 papers)
  9. Peng Wang (831 papers)
Citations (6)
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