This paper explores the problem of continual learning (CL) of vision-language
models (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.

本文探讨了在开放领域中视觉语言模型的持续学习问题，介绍了一种名为 CoLeCLIP 的新方法，通过联合学习任务提示和跨领域类别词汇来解决开放领域持续学习中的挑战，实验证明 CoLeCLIP 在开放领域持续学习中超过了最先进的方法。

CoLeCLIP：通过联合任务提示和词汇学习实现开放域持续学习

CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and  Vocabulary Learning

Relational facts are an important component of human knowledge, which are
hidden in vast amounts of text. In order to extract these facts from text,
people have been working on relation extraction (RE) for years. From early
pattern matching to current neural networks, existing RE methods have achieved
significant progress. Yet with explosion of Web text and emergence of new
relations, human knowledge is increasing drastically, and we thus require
"more" from RE: a more powerful RE system that can robustly utilize more data,
efficiently learn more relations, easily handle more complicated context, and
flexibly generalize to more open domains. In this paper, we look back at
existing RE methods, analyze key challenges we are facing nowadays, and show
promising directions towards more powerful RE. We hope our view can advance
this field and inspire more efforts in the community.

本文回顾了现有的关系抽取方法，分析了当前面临的关键挑战，并展示了通向更强大的关系抽取的有前途的方向。