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DCA-MoE: Spatially Adaptive Cross-Layer Fusion and Density-Routed Experts for Crowd Counting
Aug 15, 2026Hao WangMoe-Nd Cross-Layer Interactions
HybridSB-MoE: Dual-Domain Schrödinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement
Aug 13, 2026Zhengyi Lu, Aswini Sivakumar, Jie Hu +1Speech Enhancement Schrödinger Bridges
DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning
Aug 10, 2026Mainak Singha, Niccolò Biondi, Elisa Ricci +1Multimodal Instruction Tuning Instruction Tuning
AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding
Aug 4, 2026Shuang Liang, Hao Mark Chen, Zhiwen Mo +4Mixture-Of-Expert Inference Moe-Nd
ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment
Aug 3, 2026Nan Bi, Taoyue Wang, Lijun Yin +13D Masked Autoencoders Facial Expression Recognition
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
Jul 29, 2026Yu Chang, Anzhe Cheng, Chenwei Wu +7Multi-Modal Fusion Text Modality
MMOE: Modernizing Diffusion Transformers with Efficient Expert Design
Jul 27, 2026Yanhao Jia, Jiepeng Wang, Haibin Huang +3Mixture-Of-Expert Language Models Moe-Nd
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
Jul 6, 2026Babak Barazandeh, Subhabrata Majumdar, Vinay Prithyani +1Gradient-Based Optimization Moe-Nd
WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection
Jul 5, 2026Xian Li, Yuanhe Tian, Yang Yang +2Depression Screening
CLEAR-MoE: Shared-Basis Expert Extraction from Frozen Vision Transformers via Calibration-Driven Layer Selection
Jun 26, 2026Md Irtiza Hossain, Humaira Ayesha, Junaid Ahmed SifatVision Transformer Moe-Nd
Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search
Jun 21, 2026Yashkumar R Lukhi, Harsh Rameshbhai Moradiya, Radu Timofte +1Neural Architecture Search Moe-Nd
Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study
Jun 19, 2026Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard +2Mixture-Of-Expert Inference Moe-Nd
CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion
Jun 17, 2026Francisco Affonso, Matheus P. Angarola, Ana Luiza Mineiro +3Locomotion Terrain
DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis
Jun 15, 2026Jun-En Ding, Spencer Chen, Henry Noren +6Seizure Detection Stroke
Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
Jun 12, 2026Chufei Yan, Zhihao Cui, Yiyan Lv +3Autonomous Driving Safe Reinforcement Learning
CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
Jun 3, 2026Kailun Huang, Zikang Xie, Yanzhe Xie +8Perceptive Locomotion Policies Long-Tail Humanoid Whole-Body Control
LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling
Jun 3, 2026Wenkai Chen, Tianshu Li, Wenyong Huang +3Mixture-Of-Expert Language Models Moe-Nd
DOT-MoE: Differentiable Optimal Transport for MoEfication
Jun 1, 2026Udbhav Bamba, Arnav Chavan, Aryamaan Thakur +2Moe-Nd Parameter-Efficient Fine-Tuning Methods
DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
May 31, 2026Jiarui Feng, Hanqing Zeng, Karish Grover +11Cold Mixture-Of-Expert Models Skewed Expert Demand
MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts
May 30, 2026Vinay Edula, Priyanka BagadeBounding Box Object Detection
MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction
May 29, 2026Yinzhe Wu, Fanwen Wang, Zhenxuan Zhang +3Magnetic Resonance Imaging Reconstruction Magnetic Resonance Imaging
dMoE: dLLMs with Learnable Block Experts
May 29, 2026Sicheng Feng, Zigeng Chen, Gongfan Fang +2Moe-Nd Diffusion Language Models
Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
May 28, 2026Amirhossein Ghaffari, Saeid Sheikhi, Ekaterina GilmanTemporal Graph Neural Networks Accurate Traffic Forecasting
ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression
May 28, 2026Yilun Yao, Jiaming Pan, Elsie Dai +3Mixture-Of-Expert Language Models Moe-Nd
Pruning and Distilling Mixture-of-Experts into Dense Language Models
May 27, 2026Junhyuck Kim, Jihun Yun, Haechan Kim +3Moe-Nd Large Language Model Compression
MobileMoE: Scaling On-Device Mixture of Experts
May 26, 2026Yanbei Chen, Hanxian Huang, Ernie Chang +5Mixture-Of-Expert Language Models Mixture-Of-Expert Inference
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
May 25, 2026Junlin Yang, Tian Yu, Nicha C. Dvornek +6Intrahepatic Cholangiocarcinoma Experts
Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization
May 24, 2026Gorgi PavlovLarge Language Model Quantization Model Weights
Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEs
May 23, 2026Bo Li, Tianyu Dong, Shaolin Zhu +1Multilingual Language Models Mixture-Of-Expert Language Models
BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
May 22, 2026Jiayu Zhao, Zihan Teng, Minhao Fan +4Large Language Model Quantization Mixture-Of-Expert Language Models
SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks
May 22, 2026Yukai Yang, Chenxi Qin, Jungang Li +3Spiking Neural Networks Binary Spikes
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
May 18, 2026Yang Liu, Toan Nguyen, Flora D. SalimEfficient Continual Learning Mixture-Of-Expert Language Models
CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
May 18, 2026Muyoung Son, Yi Chen, Seungjae Yoo +2Mixture-Of-Expert Inference Multi-Gpu Systems
Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation
May 18, 2026Diego Adame, Fabian Vazquez, Jose A. Nunez +7Medical Image Segmentation Hybrid Transformer
Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing
May 14, 2026Ellwil Sharma, Arastu SharmaFluid Dynamics Wireless Foundation Models
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE
May 4, 2026Yangming Shi, Shixiang Zhu, Tao Shen +14Video Diffusion Models Multimodal Generation
MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving
May 3, 2026Zhaoyuan Su, Olatunji Ruwase, Karthik Ganesan +5Mixture-Of-Expert Inference Prefill
Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts
May 1, 2026Man Yung WongMixture-Of-Expert Routing Moe-Nd
Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
Apr 29, 2026Weihang Li, Jianchun Liu, Hongli XuMultiple Model Experts Parameter-Efficient Fine-Tuning Methods
MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression
Apr 20, 2026Libo Sun, Peixiong He, Po-Wei Harn +1Key-Value Cache Compression Large Language Model Compression
StanceMoE: Mixture-of-Experts Architecture for Stance Detection
Apr 1, 2026Abdullah Al Shafi, Md. Milon Islam, Sk. Imran Hossain +1Tweet-Level Stance Detection Mixture-Of-Experts Layer
MOOZY: A Patient-First Foundation Model for Computational Pathology
Mar 27, 2026Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal +1Pathology Foundation Models Electronic Health Record Foundation Models
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds
Jun 27, 2025Edward Chen, Sang T. Truong, Natalie Dullerud +2Pareto Frontier Preference Learning

Aug 15, 2026 · cs.CVJ/K move · Enter open · S save

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

Hao Wang

BDNRC

Crowd counting must recover reliable local density under severe variations in perspective, head scale, occlusion, and background clutter. Although modern counting objectives provide strong spatial supervision, many multi-level decoders still use spatially invariant feature fusion and apply one receptive-field pattern to every location. We propose DCA-MoE, a framework that makes both decisions content dependent while retaining a frozen DINOv3 encoder. Spatially Adaptive Layer Fusion (SALF) predicts position-wise weights over four aligned backbone features, and Density-Routed Multi-Receptive-Field Experts (DR-MoE) assigns each location a soft mixture of local, mid-range, and large-context residual experts. An EBC-style head reconstructs block density, while DMCount supervision and an auxiliary routing-balance term train the decoder without updating the backbone. On the NWPU-Crowd validation split, the strongest paired configuration, based on DINOv3 ViT-L/16, obtains 31.7 MAE and 72.2 RMSE; the matched ViT-B/16 full model obtains a paired 32.2/75.9. Cross-dataset results remain mixed, and several component baselines currently report independently selected minima from a single seed. The evidence therefore supports the feasibility of spatially adaptive fusion and routing, while broader paired and multi-seed evaluation remains necessary for causal attribution.

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