Hybrid ConvNeXt Architectures

• Hybrid ConvNeXt is a family of architectures that merge the ConvNeXt backbone with complementary modules such as attention blocks, transformers, and auxiliary backbones for enhanced feature representation.
• They employ diverse fusion techniques, including feature concatenation, logit averaging, and cross-attention, to integrate multi-scale and multi-modal information effectively.
• These architectures are applied across fields like medical imaging, IoT intrusion detection, and biometric segmentation, demonstrating significant performance gains over single-backbone models.

Searching arXiv for papers on Hybrid ConvNeXt to ground the article in the provided literature. arxiv_search(query="Hybrid ConvNeXt", max_results=10, sort_by="relevance") In the recent literature, “Hybrid ConvNeXt” denotes a family of architectures that retain ConvNeXt as a backbone, block family, or branch while combining it with complementary modules, backbones, fusion rules, decoders, or training schemes. The resulting systems span feature-concatenation classifiers, ConvNeXt–Transformer hybrids, attention-augmented ConvNeXt blocks, multi-scale encoder–decoder models, optimal-transport pyramid fusion, and co-designed self-supervised ConvNet training. Reported application areas include falcon endoscopy, diabetic foot ulcer classification, PCOS ultrasound classification, IoT intrusion detection, brain-age estimation from MRI, image tampering localization, inertial navigation, and biomedical segmentation (Panthakkan et al., 8 Jun 2025, Rifat et al., 26 Oct 2025, Hoque et al., 21 Jan 2026, Fu et al., 2022, Florindo et al., 21 May 2026).

1. Conceptual scope and lineage

Hybrid ConvNeXt is not a single canonical architecture. In the surveyed work, the term covers several distinct forms of hybridization. One form combines ConvNeXt with another pretrained backbone and fuses their outputs, as in ConvNeXt–EfficientNet for falcon disease detection and the DenConST and DenConREST ensembles for PCOS ultrasound classification (Panthakkan et al., 8 Jun 2025, Hoque et al., 21 Jan 2026). A second form inserts attention or Transformer modules into ConvNeXt stages or blocks, as in ConMatFormer, CNSCA, and ConvXformer (Rifat et al., 26 Oct 2025, Amankwah et al., 1 Sep 2025, Tariq et al., 22 Oct 2025). A third form keeps ConvNeXt as the encoder inside a larger multi-scale or multi-pathway system, such as OTFPF for brain-age estimation, ConvNeXt-FD for biomedical segmentation, and the tampering-localization model based on multi-scale ConvNeXt feature fusion (Fu et al., 2022, Florindo et al., 21 May 2026, Zhu et al., 2022). A fourth form hybridizes ConvNeXt at the training-framework level rather than by adding a second inference branch, as in ConvNeXt V2, which co-designs a fully convolutional masked autoencoder with Global Response Normalization (Woo et al., 2023).

This usage differs from a narrow identification of “hybrid” with “ConvNeXt plus Transformer.” The literature also includes hybridization with EfficientNet, DenseNet121, Swin Transformer, ResNet18, EfficientNetV2, CBAM, DANet, self-attention, channel attention, feature pyramids, optimal transport, U-Net-style skip fusion, and auxiliary fractal-dimension regression (Panthakkan et al., 8 Jun 2025, Hoque et al., 21 Jan 2026, Rifat et al., 26 Oct 2025, Fu et al., 2022, Florindo et al., 21 May 2026). A related efficiency-oriented lineage appears in InceptionNeXt, which replaces ConvNeXt’s single large-kernel depthwise token mixer with an Inception-style multi-branch depthwise module, and in earlier convolution–transformer work such as ConTNet, which motivates the local–global complementarity later reused by several ConvNeXt hybrids (Yu et al., 2023, Yan et al., 2021).

2. Architectural patterns

A recurring pattern is multi-backbone fusion. In falcon endoscopic classification, ConvNeXt and EfficientNet are both used as transfer-learning backbones, their high-level features are concatenated, and the fused representation is mapped to three-class logits by a linear or MLP head. With $f_c \in \mathbb{R}^{d_c}$ and $f_e \in \mathbb{R}^{d_e}$, the fused feature is $f = [f_c; f_e]$, and a generic classifier is written as $z = Wf + b$, followed by softmax (Panthakkan et al., 8 Jun 2025). In PCOS ultrasound classification, DenConST and DenConREST instead perform late fusion of logits from multiple branches, with uniform averaging rather than feature concatenation (Hoque et al., 21 Jan 2026).

A second pattern is stage-wise augmentation of ConvNeXt with attention and transformers. ConMatFormer uses a five-stage pipeline: a stem, ConvNeXtV2 Block ×3 + CBAM in Stages 1 and 2, ConvNeXtV2 Block ×2 + CBAM in Stage 3, ConvNeXtV2 Block + DANet in Stage 4, and Transformer Block + LayerNorm + Dropout in Stage 5, followed by Global Average Pooling + LayerNorm and a linear classifier (Rifat et al., 26 Oct 2025). This design places ConvNeXtV2 in the early and middle layers for local feature extraction, then introduces CBAM, DANet, and a Transformer block for progressively broader context aggregation.

A third pattern is intra-block hybridization. CNSCA modifies the internal ConvNeXt block by inserting self-attention and channel attention after the expansion pointwise convolution and GELU, then projecting back to the original width and adding a residual connection (Amankwah et al., 1 Sep 2025). ConvXformer adapts ConvNeXt to 1D inertial signals and pairs each ConvNeXt block with an integrated Transformer encoder inside a four-stage temporal pyramid, while retaining depthwise convolution, pointwise expansion and projection, residuals, and DropPath (Tariq et al., 22 Oct 2025).

A fourth pattern is serial fusion for structured non-image inputs. The IoT intrusion-detection model uses a 1D CNN for local numerical-feature extraction, feeds its output into an optimized lightweight ConvNeXt-Tiny block, and then applies a compact MLP head: Conv1D(filters=64, kernel=5, padding='same', activation=ReLU) → ConvNeXt-Tiny Block → Flatten → Dense(128, ReLU) → Dense(64, ReLU) → Dropout($p=0.1$) → Dense(8, Softmax) (Roshanzadeh et al., 7 Sep 2025).

A fifth pattern is encoder–decoder and pyramid hybridization. For image tampering localization, stacked ConvNeXt blocks act as a four-stage encoder, while the decoder combines a Pyramid Pooling Module, FPN-style top-down addition, and final multi-scale concatenative fusion (Zhu et al., 2022). In ConvNeXt-FD, ConvNeXt is the encoder inside a U-Net-like architecture with skip concatenations, a segmentation head, and an auxiliary fractal-dimension head (Florindo et al., 21 May 2026). In OTFPF, 3D overlapped ConvNeXt modules are combined with semi-multimodal GM/WM fusion and an optimal-transport feature-pyramid module (Fu et al., 2022).

Pattern	Representative systems	Core operator
Multi-backbone fusion	Falcon ConvNeXt–EfficientNet, DenConST, DenConREST	Feature concatenation or logit averaging
Stage-wise augmentation	ConMatFormer, ConvXformer	CBAM, DANet, Transformer encoder
Intra-block augmentation	CNSCA, InceptionNeXt	Self-attention, channel attention, Inception depthwise mixer
Serial fusion	CNN + ConvNeXt-Tiny IDS	Conv1D → ConvNeXt-Tiny
Encoder–decoder fusion	Tampering localization, ConvNeXt-FD, OTFPF	PPM/FPN, U-Net skips, OT pyramid fusion

3. Fusion operators, objectives, and optimization

The fusion operator is one of the main axes along which Hybrid ConvNeXt systems differ. In the falcon classifier, fusion is explicit feature concatenation before the classifier, with standard balanced three-class cross-entropy because the dataset is exactly balanced:

$\mathcal{L} = -\sum_{i=1}^{3} y_i \log(p_i).$

The paper explicitly notes that class-weighted cross-entropy and focal loss are not used (Panthakkan et al., 8 Jun 2025).

In the PCOS ensembles, fusion occurs at the logit level. For DenConST, the fused logit is

$$z = \frac{z_{\text{Den}} + z_{\text{Con}} + z_{\text{Swin}}}{3},$$

and for DenConREST the general formulation is

$$F(x) = \sigma\!\left(\sum_{i=1}^{5} w_i f_i(x)\right), \qquad \sum_i w_i = 1,$$

with the reported implementation using uniform averaging, $w_i = 1/5$ (Hoque et al., 21 Jan 2026). This is architecturally simpler than early feature fusion and avoids cross-branch meta-classifiers.

In EVCC, token-level fusion is performed by gated bidirectional cross-attention between a ViT branch and a lightweight ConvNeXt branch after adaptive token pruning. The recipient-token update is written as

$$Z_v' = Z_v + G_v \odot \mathrm{Attn}_{v \to c}, \qquad Z_c' = Z_c + G_c \odot \mathrm{Attn}_{c \to v},$$

where the gates are sigmoid activations of linear projections of the recipient tokens. A late dynamic router gate then interpolates between adaptive branch weights and a uniform prior (Hasan et al., 24 Nov 2025). This suggests a more selective notion of hybridization than simple concatenation.

Other hybrids treat fusion as a multi-scale geometric aggregation problem. OTFPF first unifies channels across four 3D ConvNeXt feature levels, merges them by top-down addition, and then converts tokenized multi-scale features into fixed-length embeddings with entropic-regularized Kantorovich optimal transport and Nyström kernel approximation (Fu et al., 2022). The tampering-localization model uses additive FPN fusion in the decoder and final concatenation after upsampling to the finest scale (Zhu et al., 2022).

Hybridization also appears in the objective. ConvNeXt-FD optimizes

$f_e \in \mathbb{R}^{d_e}$0

where $f_e \in \mathbb{R}^{d_e}$1 is an MSE loss on a predicted fractal-dimension map derived from differential box-counting (Florindo et al., 21 May 2026). The tampering-localization system combines focal loss and Lovász-Softmax with

$f_e \in \mathbb{R}^{d_e}$2

using $f_e \in \mathbb{R}^{d_e}$3, $f_e \in \mathbb{R}^{d_e}$4, and $f_e \in \mathbb{R}^{d_e}$5 (Zhu et al., 2022). ConvNeXt V2 hybridizes at the pretraining level by pairing ConvNeXt with a fully convolutional masked autoencoder and adding Global Response Normalization after the dimension-expansion MLP; the masked-reconstruction loss is mean-squared error computed only on masked patches (Woo et al., 2023).

4. Application domains and representative systems

Medical imaging is the most heavily represented domain. Falcon disease detection uses a balanced set of 610 labeled endoscopy images from the Sharjah Falcon Clinic, with 203 images per class for Normal, Liver Disease, and Aspergillosis, and a 126-image test set (Panthakkan et al., 8 Jun 2025). ConMatFormer addresses diabetic foot ulcer classification on DS1 (DFUC2021) and DS2 (Kaggle DFU), combining ConvNeXtV2, CBAM, DANet, and a Transformer to improve minority-class recognition under severe class imbalance (Rifat et al., 26 Oct 2025). DenConST and DenConREST target PCOS detection from ultrasound scans by fusing ConvNeXt with DenseNet121, Swin Transformer, ResNet18, and EfficientNetV2 at the logit level (Hoque et al., 21 Jan 2026). ConvNeXt-FD uses a ConvNeXt encoder and U-Net-style decoder across BUSI, DDTI, FluoCells, IDRiD, ISIC2018, and MoNuSeg (Florindo et al., 21 May 2026).

Hybrid ConvNeXt has also been applied to security and digital forensics. The IoT intrusion-detection model uses ConvNeXt-Tiny in a lightweight serial pipeline over 115 continuous numerical features from N-BaIoT, targeting eight classes including BENIGN, BASHLITE/Gafgyt variants, and Mirai variants (Roshanzadeh et al., 7 Sep 2025). The tampering-localization model uses a ConvNeXt-Base encoder pretrained on ImageNet-22K and a UPerNet-style decoder to predict pixel-level manipulation masks (Zhu et al., 2022).

A separate branch involves spatiotemporal and volumetric signals. OTFPF adapts ConvNeXt to 3D MRI with overlapped downsampling, semi-multimodal GM/WM fusion, and optimal-transport pyramid fusion for brain-age regression (Fu et al., 2022). ConvXformer adapts ConvNeXt to 1D IMU windows $f_e \in \mathbb{R}^{d_e}$6, inserting Transformer encoders into each temporal stage and coupling the model with a differentially private training mechanism based on adaptive clipping and gradient-aligned noise injection (Tariq et al., 22 Oct 2025).

Fine-grained texture and material classification is represented by CNSCA, which classifies coal fines content into seven classes using self-attention and channel attention inside ConvNeXt blocks (Amankwah et al., 1 Sep 2025). Efficiency-oriented architectural research is represented by InceptionNeXt, where ConvNeXt’s large-kernel depthwise convolution is replaced by four parallel branches: a 3×3 depthwise branch, a 1×k branch, a k×1 branch, and an identity branch (Yu et al., 2023).

5. Empirical behavior

Several studies report substantial internal gains from hybridization relative to single-backbone ConvNeXt baselines. In falcon disease detection, the ConvNeXt-only model reports average Accuracy 0.87, Precision 0.88, Recall 0.87, and F1 0.87; the EfficientNet-only model reports average Accuracy 0.94, Precision 0.95, Recall 0.95, and F1 0.94; and the Hybrid ConvNeXt–EfficientNet model reports average Accuracy 0.98, Precision 0.97, Recall 0.98, and F1 0.98 on the 126-image test set. The paper states that the strongest lift is in Liver Disease and Aspergillosis, where single models show recall or precision deficits that the concatenation largely resolves (Panthakkan et al., 8 Jun 2025).

ConMatFormer reports Accuracy 0.8961, Precision 0.9160, Recall 0.8866, and F1 0.9004 on DS1 in a single experiment, and 4-fold cross-validation results of Accuracy $f_e \in \mathbb{R}^{d_e}$7, Precision $f_e \in \mathbb{R}^{d_e}$8, Recall $f_e \in \mathbb{R}^{d_e}$9, and F1 $f = [f_c; f_e]$0. The paired t-tests show significant improvements versus Swin-T, MaxViT-T, ConvNeXtV2-T, EfficientNet-B0, and MobileNetV2, but not versus FastViT-MA36 ($f = [f_c; f_e]$1) (Rifat et al., 26 Oct 2025).

For PCOS ultrasound classification, single backbones report Accuracy 0.5645 for Swin Transformer, 0.5884 for ConvNeXt, 0.6883 for DenseNet121, 0.5937 for ResNet18, and 0.7955 for EfficientNetV2 on the 1,922-image test set after preprocessing. DenConST reports Accuracy 0.8569, Precision 0.8832, Recall 0.8747, and F1 0.8789, while DenConREST reports Accuracy 0.9823, Precision 0.9719, Recall 0.9991, and F1 0.9849 (Hoque et al., 21 Jan 2026).

In the IoT IDS setting, the serial CNN + ConvNeXt-Tiny model reports Train Accuracy 99.67%, Validation Accuracy 99.53%, Test Accuracy 99.63%, Test Loss 0.0107, and measured validation/test latency of approximately 5.52 ms/step on an ASUS TUF Gaming F15 with GTX 1650 (4 GB) (Roshanzadeh et al., 7 Sep 2025). In brain-age estimation, OTFPF reports test-set MAE 2.097, PCC 0.993, and SRCC 0.989 on 11,728 T1 MRIs aged 3–97 years (Fu et al., 2022). In biomedical segmentation, ConvNeXt-FD reports Dice 0.8040 ± 0.0324 on BUSI, 0.8013 ± 0.0208 on DDTI, 0.8510 ± 0.0254 on FluoCells, 0.9619 on IDRiD, 0.8912 on ISIC2018, and 0.8223 on MoNuSeg, with especially large gains from ImageNet pretraining on BUSI and DDTI (Florindo et al., 21 May 2026).

Hybridization can also target efficiency rather than only accuracy. InceptionNeXt-T reports training throughput 901 img/s versus ConvNeXt-T’s 575 on A100 FP32, inference throughput 2900 versus 2413, and top-1 82.3% versus 82.1% (Yu et al., 2023). ConvNeXt V2-H, under the three-step schedule of FCMAE pretraining, ImageNet-22K fine-tuning, and ImageNet-1K fine-tuning, reports 88.9% top-1 on ImageNet-1K at 512×512 using only public training data (Woo et al., 2023). These results indicate that Hybrid ConvNeXt is not restricted to downstream fusion models; it also includes co-designed architectural and pretraining hybrids that improve the underlying ConvNet family itself.

6. Limitations, misconceptions, and research directions

A common misconception is that hybridization alone establishes robustness or deployment readiness. The literature does not support that conclusion. The falcon endoscopy study reports no cross-validation, no external validation, no robustness analysis under lighting or device shifts, no calibration metrics such as Expected Calibration Error, and no computational profile including parameter counts, FLOPs, throughput, latency, or memory footprint (Panthakkan et al., 8 Jun 2025). ConMatFormer provides strong cross-validation and significance testing, but transformer hyperparameters are not explicitly listed and broader multi-center validation is still identified as needed (Rifat et al., 26 Oct 2025). DenConREST reports strong internal performance, yet no explicit ablation removing the ConvNeXt branch, no learned fusion alternatives, no statistical significance testing, and no latency measurement are provided (Hoque et al., 21 Jan 2026).

Reproducibility is also uneven. The IoT IDS study does not report the number of epochs, batch size, exact ConvNeXt-Tiny internal configuration, random seeds, or code availability (Roshanzadeh et al., 7 Sep 2025). The falcon study omits optimizer choice, learning rate, weight decay, scheduler, warmup, early stopping, gradient clipping, mixed precision, augmentation policy, normalization statistics, and exact backbone variants (Panthakkan et al., 8 Jun 2025). ConvNeXt-FD does not provide code and requires non-trivial FD-target computation, while its performance depends on dataset-specific tuning of $f = [f_c; f_e]$2 (Florindo et al., 21 May 2026).

Several hybrids introduce additional optimization or systems complexity. ConvXformer’s privacy-preserving training depends jointly on truncation rank $f = [f_c; f_e]$3, clipping threshold $f = [f_c; f_e]$4, and noise multiplier $f = [f_c; f_e]$5; the paper notes that mis-specification can yield under-clipping or over-noising, and truncated SVD adds overhead (Tariq et al., 22 Oct 2025). EVCC improves efficiency with token pruning and routing, but aggressive pruning factors such as $f = [f_c; f_e]$6 or $f = [f_c; f_e]$7 reduce accuracy relative to the moderate regime $f = [f_c; f_e]$8 (Hasan et al., 24 Nov 2025). InceptionNeXt shows that large-kernel ConvNeXt blocks can be hybridized for throughput, but speedups are device-dependent and are larger for training than for inference (Yu et al., 2023).

The surveyed work suggests several durable research directions. One is better-specified hybrid design, including full disclosure of backbone variants, fusion locations, head depth, and optimization schedules. A second is stronger validation, particularly external validation, multi-center studies, and distribution-shift testing. A third is calibration and operating-point analysis, especially in medical screening settings where ROC curves are shown qualitatively but thresholds and calibration are not reported. A fourth is hybrid efficiency analysis, including parameter counts, FLOPs, memory footprint, and latency on real deployment targets. A fifth is more principled fusion learning, such as learned routing, cross-branch attention, or structure-aware fusion, although several papers explicitly do not evaluate these alternatives (Panthakkan et al., 8 Jun 2025, Rifat et al., 26 Oct 2025, Hoque et al., 21 Jan 2026).

Across these studies, Hybrid ConvNeXt emerges less as a fixed architecture than as a design principle: preserve ConvNeXt’s hierarchical convolutional inductive bias, then add complementary mechanisms only where they improve local–global trade-offs, multi-scale fusion, modality interaction, efficiency, or task-specific supervision.