ConvNeXtV2 Specialists in Ensemble Modeling

Updated 27 November 2025

ConvNeXtV2 specialists are task-specific, lightweight CNN encoders that operate within modular ensemble frameworks for robust feature extraction.
They employ compact atto-size architectures with depthwise separable convolutions and GRN layers to enhance representational efficiency and prevent feature collapse.
Their ensemble integration via scalable fusion enables efficient transfer, pruning, and federated training across applications such as remote sensing and medical imaging.

ConvNeXtV2 specialists are lightweight, task-specific convolutional neural network (CNN) feature encoders based on the ConvNeXtV2 architecture, utilized within ensemble frameworks to achieve generalist and robust feature extraction. These architectures, epitomized by their systematic use in modular, federated, and resource-efficient ensemble systems such as the Ensemble-of-Specialists Foundation Model (EoS-FM), demonstrate how small, dedicated ConvNeXtV2 instances—each focused on a discrete dataset or modality—can collectively rival or surpass monolithic foundation models in both performance and sustainability for domains such as remote sensing, medical imaging, and histopathology (Adorni et al., 26 Nov 2025, Woo et al., 2023, Yurdakul et al., 24 Feb 2025, Yurdakul et al., 11 Sep 2025).

1. Architectural Specification

ConvNeXtV2 specialists are instantiated most commonly as “Atto” size models (≈3.4–3.7 million parameters), leveraging their compactness and high representational efficiency. The canonical ConvNeXtV2 block comprises:

A 4×4 strided patch-embedding Convolutional stem:

$F_0 = \mathrm{Conv2d}(x; \text{kernel}=4,\,\text{stride}=4,\,\text{out\_channels}=C_1)$

Multiple stages (typically four) of residual blocks, each structured as:
1. Depthwise separable 7×7 convolution:
$y = \mathrm{DWConv}_{7\times7}(z)$

LayerNorm or BatchNorm:

$\bar{y} = \mathrm{Norm}(y)$
Pointwise 1×1 expansion, GELU activation, pointwise 1×1 projection.
Residual connection $z + \bar{y}$ .

The GRN (Global Response Normalization) layer is a defining innovation, introduced after the expansion MLP, enhancing channel competition and addressing feature collapse under self-supervised regimes: $g_i = \|X_i\|_2; \quad n_i = \frac{g_i}{\sum_j g_j + \epsilon}; \quad \widetilde{X}_i(h,w) = X_i(h,w)[\gamma_i n_i + \beta_i] + X_i(h,w)$ with learnable scale $\gamma$ and bias $\beta$ .

Specialists may be prefixed by a band adaptation layer to map heterogeneous input modalities to the 3-channel format expected by the ConvNeXtV2 stem. For example, Sentinel-2 multispectral or SAR bands are selected or duplicated according to task requirements (Adorni et al., 26 Nov 2025).

2. Specialist Training Protocols

Each ConvNeXtV2 specialist is trained (or fine-tuned, using MAE-pretrained weights) exclusively on a single dataset or task, adhering to the following loss: $\mathcal{L}_i = \mathbb{E}_{(x,y)\sim\mathcal{D}_i}[\ell(h_i(E_i(x)),\,y)]$ where $h_i$ is a task-specific head:

Classification: global average pool → linear layer.
Segmentation: UPerNet or a lightweight decoder head.
Change detection: Siamese difference head.

The training objective, although algorithmically decoupled across specialists, can be expressed as a multi-task sum with uniform weighting: $\mathcal{L}_{\mathrm{total}} = \sum_{i=1}^{N} \alpha_i \mathcal{L}_i; \quad \alpha_i=1$ No modifications are made within ConvNeXtV2 encoders themselves; each specialist is independent and frozen after training (Adorni et al., 26 Nov 2025).

3. Ensemble Construction and Feature Fusion

The integration mechanism for ConvNeXtV2 specialists into a unified feature extractor relies on modular ensemble fusion:

Each specialist emits feature maps $F_i = E_i(x) \in \mathbb{R}^{C\times H\times W}$ .
Non-parametric batch normalization (scale/bias-free) is applied to each feature map $\hat{F}_i = \mathrm{BN}(F_i)$ for cross-specialist alignment.
A learnable scalar weight $w_i \geq 0$ modulates each $\hat{F}_i$ , with optional top- $k$ encoder selection by $w_i$ magnitude:

$\tilde{F}_i = \begin{cases} w_i \hat{F}_i & \text{if } i \in \mathrm{Top}(k,w) \ 0 & \text{otherwise} \end{cases}$

Channel-wise concatenation across selected $\tilde{F}_i$ is followed by a $1\times1$ convolution for feature fusion:

$f(x) = \mathrm{Conv}_{1\times1}([\tilde{F}_1, \dots, \tilde{F}_N])$

At downstream adaptation/fine-tuning time, only the fusion layer and $w_i$ are updated; all ConvNeXtV2 specialists remain frozen. This separation enables efficient transfer, pruning, and incremental extension (Adorni et al., 26 Nov 2025).

4. Modular Extensibility, Federated Training, and Pruning

ConvNeXtV2 specialists are inherently modular:

Task/Modality Extension: New specialists can be trained on additional tasks or modalities and appended to the ensemble, requiring only minimal fusion-layer and weight re-tuning.
Federated Collaboration: Individual institutions may train their own specialists on private data, sharing only frozen ConvNeXtV2 weights; central or federated updates to ensemble weights and fusion are supported.
Pruning: Top- $k$ specialist selection at fine-tuning enables reducing inference cost by discarding low-contribution encoders, while retaining or compressing only those with high $w_i$ .

This design directly addresses sustainability, computational footprint, and collaborative scalability—especially significant for small institutions or resource-constrained environments (Adorni et al., 26 Nov 2025).

5. Empirical Performance and Efficiency Characteristics

ConvNeXtV2 specialist ensembles demonstrate competitive or superior performance relative to much larger monolithic models:

On the Pangaea suite of 11 remote sensing tasks:
- Full EoS-FM (21 specialists, 72M params): Avg DTB = 3.81, beating CROMA (303M params).
- EoS-FM Small (6 specialists, 22M params): Avg DTB = 7.29, competitive with single-task ResNet-50.
- Under 10% label availability, EoS-FM full: Avg DTB = 4.70 vs. 9–13 for leading frozen baselines; small ensemble: Avg DTB ≈ 5.86.
- Inference cost scales linearly with active specialists; $k$ out of $N$ allows compute to be reduced by a factor of $k/N$ .
- Carbon emissions during large-scale training are reported to be an order of magnitude below billion-parameter RSFMs (Adorni et al., 26 Nov 2025).

Table: Model Footprint and Performance (Pangaea 11 Task Benchmark)

Model	# Parameters	Avg DTB	Label Scarcity (10%)
EoS-FM (21 spec.)	72M	3.81	4.70
EoS-FM Small (6)	22M	7.29	≈5.86
CROMA	303M	>3.81	9–13

6. Applications, Variants, and Integrations

ConvNeXtV2 specialists are increasingly adopted in multiple domains and as building blocks in hybrid architectures:

In medical imaging, swapping MBConv with ConvNeXtV2 in lightweight vision transformers (e.g., MaxGlaViT) achieved 1.94 percentage point absolute accuracy gains for glaucoma stage classification, and 4.10 pp overall when combined with ECA-stem block, using a model of <7M parameters (Yurdakul et al., 24 Feb 2025).
CoAtNeXt incorporates enhanced ConvNeXtV2 blocks with CBAM attention in place of MBConv within CoAtNet, yielding substantial improvements in gastric tissue classification performance, outperforming all tested CNN and ViT baselines with only 18.8M parameters (Yurdakul et al., 11 Sep 2025).
In both scenarios, ConvNeXtV2 specialists’ large 7×7 depthwise convolution, GRN, and modern normalization/activation consistently yield parameter and FLOP reductions compared to MBConv, and ablations identify the GRN layer as the principal driver preventing feature collapse under self-supervised or small-sample regimes (Woo et al., 2023, Yurdakul et al., 11 Sep 2025).

7. Significance and Future Directions

The ConvNeXtV2 specialist paradigm systematically demonstrates that carefully engineered, compact convolutional encoders—trained as single-task experts—can, when aggregated via lightweight feature fusion, achieve foundation-level generalist performance. This strategy provides direct solutions for model efficiency, interpretability, and sustainable scaling—challenges intrinsic to ever-expanding monolithic foundation models.

A plausible implication is that the “ensemble-of-specialists” approach, with ConvNeXtV2 as the archetypal specialist, will serve as a reference model for scalable and federated feature extraction in domains where heterogeneous data, privacy, and sustainability are critical, further motivating modular architectures over unitary scaling (Adorni et al., 26 Nov 2025, Woo et al., 2023, Yurdakul et al., 24 Feb 2025, Yurdakul et al., 11 Sep 2025).