  Multi-label image recognition is a fundamental task in computer vision.
Recently, Vision-Language Models (VLMs) have made notable advancements in this
area. However, previous methods fail to effectively leverage the rich knowledge
in language models and often incorporate label semantics into visual features
unidirectionally. To overcome these problems, we propose a Split-and-Synthesize
Prompting with Gated Alignments (SSPA) framework to amplify the potential of
VLMs. Specifically, we develop an in-context learning approach to associate the
inherent knowledge from LLMs. Then we propose a novel Split-and-Synthesize
Prompting (SSP) strategy to first model the generic knowledge and downstream
label semantics individually and then aggregate them carefully through the
quaternion network. Moreover, we present Gated Dual-Modal Alignments (GDMA) to
bidirectionally interact visual and linguistic modalities while eliminating
redundant cross-modal information, enabling more efficient region-level
alignments. Rather than making the final prediction by a sharp manner in
previous works, we propose a soft aggregator to jointly consider results from
all image regions. With the help of flexible prompting and gated alignments,
SSPA is generalizable to specific domains. Extensive experiments on nine
datasets from three domains (i.e., natural, pedestrian attributes and remote
sensing) demonstrate the state-of-the-art performance of SSPA. Further analyses
verify the effectiveness of SSP and the interpretability of GDMA. The code will
be made public.
