  The newly proposed Generalized Referring Expression Segmentation (GRES)
amplifies the formulation of classic RES by involving multiple/non-target
scenarios. Recent approaches focus on optimizing the last modality-fused
feature which is directly utilized for segmentation and object-existence
identification. However, the attempt to integrate all-grained information into
a single joint representation is impractical in GRES due to the increased
complexity of the spatial relationships among instances and deceptive text
descriptions. Furthermore, the subsequent binary target justification across
all referent scenarios fails to specify their inherent differences, leading to
ambiguity in object understanding. To address the weakness, we propose a
$\textbf{H}$ierarchical Semantic $\textbf{D}$ecoding with $\textbf{C}$ounting
Assistance framework (HDC). It hierarchically transfers complementary modality
information across granularities, and then aggregates each well-aligned
semantic correspondence for multi-level decoding. Moreover, with complete
semantic context modeling, we endow HDC with explicit counting capability to
facilitate comprehensive object perception in multiple/single/non-target
settings. Experimental results on gRefCOCO, Ref-ZOM, R-RefCOCO, and RefCOCO
benchmarks demonstrate the effectiveness and rationality of HDC which
outperforms the state-of-the-art GRES methods by a remarkable margin. Code will
be available $\href{https://github.com/RobertLuo1/HDC}{here}$.
