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arXiv Computer Vision By Hao Wang

DCA-MoE: Spatially Adaptive Cross-Layer Fusion and Density-Routed Experts for Crowd Counting

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DCA-MoE is a crowd‑counting framework that keeps a frozen DINOv3 encoder while introducing two content‑dependent modules: Spatially Adaptive Layer Fusion (SALF) assigns position‑wise weights to four backbone features, and Density‑Routed Multi‑Receptive‑Field Experts (DR‑MoE) mixes local, mid‑range, and large‑context residual experts for each location. An EBC‑style head reconstructs block density, and the model is trained with DMCount supervision plus a routing‑balance term. On the NWPU‑Crowd validation split, the best configuration based on DINOv3 ViT‑L/16 achieves 31.7 MAE and 72.2 RMSE, while the ViT‑B/16 full model records 32.2/75.9.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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