Solving multi-label recognition (MLR) for images in the low-label regime is a
challenging task with many real-world applications. Recent work learns an
alignment between textual and visual spaces to compensate for insufficient
image labels, but loses accuracy because of the limited amount of available MLR
annotations. In this work, we utilize the strong alignment of textual and
visual features pretrained with millions of auxiliary image-text pairs and
propose Dual Context Optimization (DualCoOp) as a unified framework for
partial-label MLR and zero-shot MLR. DualCoOp encodes positive and negative
contexts with class names as part of the linguistic input (i.e. prompts). Since
DualCoOp only introduces a very light learnable overhead upon the pretrained
vision-language framework, it can quickly adapt to multi-label recognition
tasks that have limited annotations and even unseen classes. Experiments on
standard multi-label recognition benchmarks across two challenging low-label
settings demonstrate the advantages of our approach over state-of-the-art
methods.

本论文提出了一种名为 DualCoOp 的统一框架，用于解决多标签识别的低标记问题，通过利用文本和视觉特征之间的强对齐，通过将类别名作为部分的语言输入来编码正负上下文，从而适应具有有限注释和未知类别的多标签识别任务，在标准多标签识别基准测试中，超过了现有方法的优点。