解决遮挡下物体计数难题，重建被遮挡部分特征以提升精度

Object counting has achieved remarkable success on visible instances, yet state-of-the-art (SOTA) methods fail under occlusion. This failure stems from a fundamental architectural limitation where backbone networks encode occluding surfaces rather than target objects, thereby corrupting the feature representations required for accurate enumeration. To address this, we present CountOCC, an amodal counting framework that explicitly reconstructs occluded object features through hierarchical multimodal guidance. Rather than accepting degraded encodings, we synthesize complete representations by integrating spatial context from visible fragments with semantic priors from text and visual embeddings, generating features at occluded locations across multiple pyramid levels. We further introduce a visual equivalence objective that enforces consistency in attention space, ensuring that both occluded and unoccluded views of the same scene produce spatially aligned gradient-based attention maps. Together, these complementary mechanisms preserve discriminative properties essential for accurate counting under occlusion. For rigorous evaluation, we establish occlusion-augmented versions of FSC-147 and CARPK (FSC-147-OCC and CARPK-OCC). CountOCC achieves SOTA performance on FSC-147-OCC with 26.72% and 20.80% MAE reduction over prior baselines under occlusion in validation and test, respectively. CountOCC also demonstrates exceptional generalization by setting new SOTA results on CARPK-OCC with 49.89% MAE reduction and on CAPTURe-Real with 28.79% MAE reduction, validating robust amodal counting.