  In computer vision, multi-label recognition are important tasks with many
real-world applications, but classifying previously unseen labels remains a
significant challenge. In this paper, we propose a novel algorithm, Aligned
Dual moDality ClaSsifier (ADDS), which includes a Dual-Modal decoder
(DM-decoder) with alignment between visual and textual features, for
open-vocabulary multi-label classification tasks. Then we design a simple and
yet effective method called Pyramid-Forwarding to enhance the performance for
inputs with high resolutions. Moreover, the Selective Language Supervision is
applied to further enhance the model performance. Extensive experiments
conducted on several standard benchmarks, NUS-WIDE, ImageNet-1k, ImageNet-21k,
and MS-COCO, demonstrate that our approach significantly outperforms previous
methods and provides state-of-the-art performance for open-vocabulary
multi-label classification, conventional multi-label classification and an
extreme case called single-to-multi label classification where models trained
on single-label datasets (ImageNet-1k, ImageNet-21k) are tested on multi-label
ones (MS-COCO and NUS-WIDE).
