  Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive
capabilities in open-vocabulary classification. The class token in the image
encoder is trained to capture the global features to distinguish different text
descriptions supervised by contrastive loss, making it highly effective for
single-label classification. However, it shows poor performance on multi-label
datasets because the global feature tends to be dominated by the most prominent
class and the contrastive nature of softmax operation aggravates it. In this
study, we observe that the multi-label classification results heavily rely on
discriminative local features but are overlooked by CLIP. As a result, we
dissect the preservation of patch-wise spatial information in CLIP and proposed
a local-to-global framework to obtain image tags. It comprises three steps: (1)
patch-level classification to obtain coarse scores; (2) dual-masking attention
refinement (DMAR) module to refine the coarse scores; (3) class-wise
reidentification (CWR) module to remedy predictions from a global perspective.
This framework is solely based on frozen CLIP and significantly enhances its
multi-label classification performance on various benchmarks without
dataset-specific training. Besides, to comprehensively assess the quality and
practicality of generated tags, we extend their application to the downstream
task, i.e., weakly supervised semantic segmentation (WSSS) with generated tags
as image-level pseudo labels. Experiments demonstrate that this
classify-then-segment paradigm dramatically outperforms other annotation-free
segmentation methods and validates the effectiveness of generated tags. Our
code is available at https://github.com/linyq2117/TagCLIP.
