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arXiv Computer Vision

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

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.

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arXiv Computer Vision
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MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation

MEOX is a compact multimodal masked autoencoder designed for Earth Observation that uses a 2.939 million‑parameter encoder and 3.115 million total parameters. It incorporates sensor‑specific adapters, explicit validity signals, and a shared sparse‑expert block to maintain modality‑dependent processing before a learned patch‑wise fusion, followed by fourteen encoder blocks that process a single spatial sequence with four metadata tokens. Pretrained on 1.228 million MMEarth64 samples, MEOX achieves strong performance on GEO‑Bench tasks, surpassing prior CSMoE results, and demonstrates effective sensor‑flexible representation learning with a modest parameter budget.

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arXiv AI
Aug 18

Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation

arXiv:2608. 15647v1 Announce Type: cross Abstract: Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult.

By Shuaishuai Cao, Meng Tang, Shuwei Peng, Xuan Liu, Min Huang, Jie Chen, Jiacheng Niu, Yong Chen, Edore Akpokodje, Hui Lin
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arXiv AI
Aug 11

ATLASFusion: Aggregation Tracking with Location-Aware Sparse Fusion for Robust Spatio-Temporal Multi-View Pedestrian Tracking

arXiv:2509. 08421v2 Announce Type: replace-cross Abstract: For multimedia spatial intelligence through time, multi-view multi-object tracking (MVMOT) suffers from persistent challenges in maintaining consistent object identities across different camera views, leading to tracking inaccuracies.

By Keisuke Toida, Taigo Sakai, Takeshi Nakamura, Hiroshi Shimizu, Kazuhiro Hotta
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arXiv Computer Vision
Sep 7

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods

The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.

By Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, Wanli Ouyang, Antoni B. Chan
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arXiv Computer Vision
Oct 2

FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains

FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.

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arXiv AI
Oct 26

Rethinking Multi-View Fusion for Urban Representation Learning

The paper "Rethinking Multi-View Fusion for Urban Representation Learning" introduces PRISM, a framework that addresses two key issues in urban data fusion: varying modality utility across regions and persistent gradient imbalance among heterogeneous objectives. PRISM employs sparse Mixture-of-Experts routing for region-specific view allocation and a PID-inspired regulation mechanism to reduce gradient conflicts during training. Experiments on three large-scale urban datasets demonstrate that PRISM achieves competitive performance while offering more stable optimization and balanced region representations.

By Sean Bin Yang, Zongyi Xu, Jilin Hu, Ying Sun, Kristian Torp, Bin Yang, Torben Bach Pedersen, Christian S. Jensen
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