For continual learning, text-prompt-based methods leverage text encoders and learnable prompts to encode semantic features for sequentially arrived classes over time. A common challenge encountered by existing works is how to learn unique text prompts, which implicitly carry semantic information of new classes, so that the semantic features of newly arrived classes do not overlap with those of trained classes, thereby mitigating the catastrophic forgetting problem. To address this challenge, we propose a novel approach Prototype-guided Text Prompt Selection (ProTPS)'' to intentionally increase the training flexibility thus encouraging the learning of unique text prompts. Specifically, our ProTPS learns class-specific vision prototypes and text prompts. Vision prototypes guide the selection and learning of text prompts for each class. We first evaluate our ProTPS in both class incremental (CI) setting and cross-datasets continual (CDC) learning setting. Because our ProTPS achieves performance close to the upper bounds, we further collect a real-world dataset with 112 marine species collected over a span of six years, named Marine112, to bring new challenges to the community. Marine112 is authentically suited for the class and domain incremental (CDI) learning setting and is under natural long-tail distribution. The results under three settings show that our ProTPS performs favorably against the recent state-of-the-art methods. The implementation code and Marine112 dataset will be released upon the acceptance of our paper.

翻译：摘要：针对持续学习任务，基于文本提示的方法利用文本编码器和可学习提示，对随时间顺序到达的新类别进行语义特征编码。现有工作面临的核心挑战在于：如何学习蕴含新类别语义信息的独特文本提示，使得新类别语义特征与已训练类别特征不发生重叠，从而缓解灾难性遗忘问题。为应对这一挑战，我们提出新颖的"原型引导文本提示选择"方法（ProTPS），通过有意增强训练灵活性，促进独特文本提示的学习。具体而言，ProTPS学习类别特定的视觉原型与文本提示，其中视觉原型引导各类别文本提示的选择与学习过程。我们首先在类增量（CI）设置与跨数据集持续学习（CDC）设置下评估ProTPS。鉴于该方法性能接近上限，我们进一步收集了跨越六年、包含112个海洋物种的真实数据集Marine112，为领域带来新挑战。该数据集天然适配类域增量（CDI）学习设置，并呈现自然长尾分布。三种设置下的实验结果表明，ProTPS在性能上优于近期最先进方法。实现代码与Marine112数据集将在论文被接收后开源。