Multi-label image recognition in the low-label regime is a task of great
challenge and practical significance. Previous works have focused on learning
the alignment between textual and visual spaces to compensate for limited image
labels, yet may suffer from reduced accuracy due to the scarcity of
high-quality multi-label annotations. In this research, we leverage the
powerful alignment between textual and visual features pretrained with millions
of auxiliary image-text pairs. We introduce an efficient and effective
framework called Evidence-guided Dual Context Optimization (DualCoOp++), which
serves as a unified approach for addressing partial-label and zero-shot
multi-label recognition. In DualCoOp++ we separately encode evidential,
positive, and negative contexts for target classes as parametric components of
the linguistic input (i.e., prompts). The evidential context aims to discover
all the related visual content for the target class, and serves as guidance to
aggregate positive and negative contexts from the spatial domain of the image,
enabling better distinguishment between similar categories. Additionally, we
introduce a Winner-Take-All module that promotes inter-class interaction during
training, while avoiding the need for extra parameters and costs. As DualCoOp++
imposes minimal additional learnable overhead on the pretrained vision-language
framework, it enables rapid adaptation to multi-label recognition tasks with
limited annotations and even unseen classes. Experiments on standard
multi-label recognition benchmarks across two challenging low-label settings
demonstrate the superior performance of our approach compared to
state-of-the-art methods.

在低标签情况下，利用预训练的图文特征对部分类别和零样本多标签识别进行了高效准确的建模和优化，通过引入证据引导的双重上下文优化 (DualCoOp++) 框架，分别对目标类别的证据、正例和反例进行编码，提高了在低样本情况下目标类别的区分度和相关图像的挖掘能力，实现了比现有方法更优异的性能。

DualCoOp++：有限标注下快速、高效的多标签识别适应性

DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition  with Limited Annotations

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。