  Zero-Shot Object Counting (ZSOC) aims to count referred instances of
arbitrary classes in a query image without human-annotated exemplars. To deal
with ZSOC, preceding studies proposed a two-stage pipeline: discovering
exemplars and counting. However, there remains a challenge of vulnerability to
error propagation of the sequentially designed two-stage process. In this work,
an one-stage baseline, Visual-Language Baseline (VLBase), exploring the
implicit association of the semantic-patch embeddings of CLIP is proposed.
Subsequently, the extension of VLBase to Visual-language Counter (VLCounter) is
achieved by incorporating three modules devised to tailor VLBase for object
counting. First, Semantic-conditioned Prompt Tuning (SPT) is introduced within
the image encoder to acquire target-highlighted representations. Second,
Learnable Affine Transformation (LAT) is employed to translate the
semantic-patch similarity map to be appropriate for the counting task. Lastly,
the layer-wisely encoded features are transferred to the decoder through
Segment-aware Skip Connection (SaSC) to keep the generalization capability for
unseen classes. Through extensive experiments on FSC147, CARPK, and PUCPR+, the
benefits of the end-to-end framework, VLCounter, are demonstrated.
