Vision MoE: Scalable Mixture-of-Experts Models

Updated 13 October 2025

Vision MoE is a neural network architecture that leverages sparse, learnable routing to direct inputs to specialized expert networks.
It employs conditional computation by activating only a subset of experts per input, significantly enhancing scalability and parameter efficiency.
Advanced training strategies and hardware co-design are integrated to balance expert load and improve inference speed in diverse vision applications.

A Vision Mixture-of-Experts (V-MoE) model is a neural network architecture in which sparse, learnable routing modules direct tokens or images to specialized expert networks, typically within the feed-forward layers of Vision Transformers (ViTs) or other vision architectures. V-MoE leverages conditional computation, activating only a subset of the model’s total parameters per input, to achieve substantial scalability, improved parameter efficiency, and strong performance across vision and vision-language tasks. This paradigm has demonstrated scalability rivaling and exceeding that of dense models in terms of accuracy/FLOPs trade-off, transferability, and applicability to both large-scale and resource-constrained environments.

1. Architectural Fundamentals and Routing Methods

V-MoE replaces select feed-forward (MLP) modules in conventional Vision Transformers with sparse Mixture-of-Experts layers. The generic MoE layer processes an input $x \in \mathbb{R}^D$ as

$\text{MoE}(x) = \sum_{i=1}^E g(x)_i \cdot e_i(x),$

where $E$ is the number of experts, $e_i$ an MLP with unique parameters, and $g(x) \in \mathbb{R}^E$ are gating weights determined by a sparse router. Typically, routing is formulated as:

$g(x) = \text{TOP}_k(\mathrm{softmax}(W x + \epsilon)),$

with $W$ a learnable projection, $\epsilon$ small Gaussian noise, and TOP $_k$ keeping only the $k \ll E$ highest-scoring weights, ensuring that only a fraction of all experts are activated per token. This design is extensible: in vision-language settings, modality-aware or token-type-aware routers enable further specialization.

Architectural variants exist:

Token-level routing (canonical in V-MoE (Riquelme et al., 2021)): each input patch or token is routed independently, enabling fine-grained specialization.
Vision-language MoE (e.g., (Shen et al., 2023)): splits experts into modality-specific (vision/text) and multimodal groups, each covering their respective input types.
Modality-aware MoE (e.g., EVE (Chen et al., 2023)): introduces modality-specific biases in the router to enhance token-type discrimination.
Global/image-level routing for resource-constrained scenarios (Mobile V-MoE (Daxberger et al., 2023), ViMoE (Han et al., 21 Oct 2024)): routes at the image rather than token level to reduce overhead.

A defining feature is the explicit capacity function, which controls the maximum number of tokens routed to each expert per batch:

$B_e = \operatorname{round}\left(\frac{k \cdot N \cdot P \cdot C}{E}\right),$

with $N$ images per batch, $P$ tokens per image, $C$ a tunable capacity ratio.

2. Training Strategies and Model Stabilization

Training V-MoE introduces instabilities, primarily due to expert imbalance (expert collapse) and unreliable routing. To address these, the following strategies are adopted:

Auxiliary Losses:
- Importance Loss: promotes uniform expert usage by penalizing high variance in expert loading.
- Load Loss: ensures each expert processes a comparable number of tokens.
- Combined “v-loss” (Shen et al., 2023): a weighted sum of importance and load losses, empirically observed to stabilize MoE training.
Batch Prioritized Routing (Riquelme et al., 2021, Shen et al., 2023): sorts all tokens in a batch by routing confidence, admitting the most informative tokens under capacity constraints and “dropping” lower-scoring ones for adaptive compute.
Super-class Guidance (Daxberger et al., 2023): in mobile/efficient variants, auxiliary supervision using super-class (meta-class) labels provides semantic structure to the router’s decisions, increasing both accuracy and the balance of expert specialization.
Shared Expert Augmentation (Han et al., 21 Oct 2024): includes a dedicated “shared expert” in each MoE layer to capture common knowledge, improving convergence and mitigating the negative effects of naive MoE placement.
Progressive Pre-Alignment and Knowledge Fusion (Yang et al., 12 Mar 2025): for heterogeneous expert matrices, knowledge from coarse-to-fine tasks is progressively aligned, minimizing interference and catastrophic forgetting.

Table: Stabilization Techniques in V-MoE

Technique	Principle	Use Case
Auxiliary Load Losses	Encourage balanced expert utilization	All V-MoE
Batch Prioritized Routing	Prioritize high-confidence tokens	All, adaptive compute
Super-class Guidance	Semantic supervision for routing	Mobile, efficient
Shared Expert	Aggregates common knowledge, reduces instability	All, deep networks

3. Scalability, Performance, and Efficiency

The sparsity of V-MoE layers allows model parameter counts to be increased super-linearly relative to compute cost. For example, (Riquelme et al., 2021) demonstrates that a 15B parameter V-MoE achieves 90.35% top-1 accuracy on ImageNet, matching or exceeding state-of-the-art dense ViT models while requiring only half the inference FLOPs. Upstream metrics on JFT-300M (precision@1) and few-shot transfer further evidence high sample efficiency and representation quality.

Empirical results show:

In mobile and compact variants (Daxberger et al., 2023), routing entire images to experts achieves +3.39% to +4.66% top-1 gains on ImageNet at minimal inference cost.
In multimodal and vision-language settings (Shen et al., 2023, Chen et al., 2023), sparse-VLMs reach or exceed dense model performance on VQA, NLVR2, and COCO/Flickr30K retrieval, while having only a fraction of the computational and memory requirements per inference call.
EVE (Chen et al., 2023) demonstrates a 3.5x training speedup over contrastive and matching-loss methods via unified masked-signal modeling and modality-aware routing, without performance loss.

Batch-level adaptivity via prioritized routing and slack in the capacity ratio $C$ allows dynamic performance/computation trade-off: reducing $C$ at inference allows for deliberate token dropping, lowering load and runtime with smooth accuracy decay.

4. Design Variations and Application Domains

The conceptual flexibility of V-MoE supports a diversity of use cases:

Dense-to-MoE Transfer (Zhu et al., 7 Jun 2024): MoE Jetpack converts pre-trained dense checkpoints into MoE models via checkpoint recycling, combining direct channel selection, co-activation graph partitioning, and adaptive SpheroMoE layers for rapid convergence and accuracy gains with unchanged FLOPs profiles.
Shared and Heterogeneous Experts: Specialized expert pools for vision, language, and fused modalities (e.g., MoME Transformer (Bao et al., 2021)) and progressive, task-aligned expert matrices (Astrea (Yang et al., 12 Mar 2025)) extend MoE to vision-language and multi-task environments.
Connector-Layer MoE (Pang et al., 30 Jul 2025): MoCHA introduces MoE at the multimodal connector, using a router-and-expert scheme to align and selectively blend representations from heterogeneous vision backbones before fusion with LLMs; Hierarchical Group Attention further prunes redundancy and enhances cross-encoder integration.

V-MoE models are deployed in image classification, semantic segmentation, visual question answering, image-text retrieval, document/table/chart understanding, and vision-centric instruction following. Mobile V-MoE and FPGA-accelerated UbiMoE (Dong et al., 8 Feb 2025) demonstrate V-MoE’s suitability for real-time, edge, and embedded inference.

5. Hardware Efficiency and Inference Optimization

V-MoE’s intrinsic conditional computation pattern benefits from co-designed hardware and inference optimization:

Hardware Acceleration: UbiMoE (Dong et al., 8 Feb 2025) demonstrates 1.34x–3.35x throughput and 1.54x–1.75x energy efficiency improvements versus baselines on FPGAs, with hybrid streaming kernels fusing attention and MoE blocks. Optimized balancing and double buffering keep overall latency bounded.
GPU Inference (Chitty-Venkata et al., 24 Aug 2025): Fused MoE operations, speculative decoding, and quantization to FP8 precision increase throughput by 12–30% in vision MoE models (DeepSeek, Qwen, Mixtral). Tensor parallelism scales nearly linearly across GPUs, though memory bandwidth and load imbalance remain bottlenecks for high expert counts. Dynamic expert pruning (via LExI (Chitty-Venkata et al., 2 Sep 2025)) reallocates top-k per layer, improving throughput and accuracy by focusing activation on layers where expert diversity is most crucial.
Practical Constraints: Expert routing, while enabling parameter scalability, incurs overhead from non-uniform expert workload and token-to-expert assignment; this can be alleviated by, e.g., LExI’s sensitivity-based assignment or hardware-aware expert placement.

6. Open Issues and Research Directions

The field continues to explore outstanding challenges and extensions:

Expert Specialization and Routing: Determining optimal placement and granularity of MoE layers (shallow vs. deep, token vs. image-level) remains an active area (Han et al., 21 Oct 2024). Strategies for routing heterogeneous tokens (e.g., leveraging super-class or dynamic multi-stage alignment (Yang et al., 12 Mar 2025)) and non-Euclidean embedding spaces (Zhang et al., 16 Sep 2025) offer new design axes.
Conditional Capacity and Adaptive Routing: Dynamic adjustment of capacity ratios, adaptive expert numbers, and layerwise allocation per inference batch (as in LExI) promise further efficiency gains, particularly under real-world constraints.
Modality-aware Extensions: Vision-language MoE models harness modality-segregated and cross-modal experts, with explicit routing based on input type or contextual grounding (Bao et al., 2021, Chen et al., 2023, Zhang et al., 16 Sep 2025). Hyperbolic inter-modality experts (Zhang et al., 16 Sep 2025) are proposed for better modeling hierarchical multimodal relationships.
Transferability and Generalization: Ensuring V-MoE representations transfer across tasks is an open challenge, with empirical evidence that careful expert sharing and pretraining (e.g., stagewise or progressive pre-alignment) can help mitigate task forgetting and improve generalization (Han et al., 21 Oct 2024, Yang et al., 12 Mar 2025).
Hardware and Software Co-design: Further gains depend on optimizing memory/compute partitioning, support for sparse and dynamic routing in frameworks, and deeper integration of MoE logic into parallel/distributed system software (Dong et al., 8 Feb 2025, Chitty-Venkata et al., 24 Aug 2025).

7. Summary Table: Key Vision MoE Model Characteristics

Property	V-MoE (Canonical)	Mobile V-MoE	ViMoE	Vision-Language MoE
Routing Granularity	Token/patch	Image/global	Token/class/image	Token and modality aware
Expert Placement	Select FFN blocks	Final blocks	Deep blocks	Per modality/fusion block
Stabilization	Load + import. loss, BPR	Super-class suggested	Shared expert	Aux. losses, staged prealign
Task Domains	Image classif., few-shot	Mobile CV	Image/segment.	VQA, retrieval, OCR, doc QA
Compute Saving	up to 2×+	up to 4.6%↑ acc @ 54M FLOPs	1.1%↑ DINOv2	Comparable to SOTA

All cells summarize direct data points; “acc” denotes classification accuracy.

Vision Mixture-of-Experts models represent a mature architectural paradigm for scaling, specializing, and efficiently deploying high-capacity vision and vision-language networks. They combine conditional computation for resource savings, flexible expert/module specialization for strong transfer and multitask performance, and compatibility with emerging hardware and software frameworks. Technical advances continue to refine expert allocation, routing methods, and integration with downstream systems, pointing toward broader adoption across computer vision applications.