  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.
