Conv-SE-NeXt Feature Extraction Block

• Conv-SE-NeXt is a modular neural network unit that integrates depth-wise or multi-scale convolutions with channel-wise Squeeze-and-Excitation attention to enhance discriminative power.
• It employs multi-branch designs and residual connections to aggregate spatial features while optimizing computational efficiency and reducing model complexity.
• Empirical benchmarks show that these blocks improve accuracy in tasks like image segmentation, speech recognition, and biomedical signal processing with lower resource costs.

A Conv-SE-NeXt Feature Extraction Block is a modular neural network unit synthesizing advances in convolutional processing and channel attentional recalibration, chiefly through the fusion of depth-wise separable convolutions and Squeeze-and-Excitation (SE) mechanisms. It has emerged as an efficient, highly discriminative backbone component in the context of image, speech, biomedical signal, and 3D point cloud domains. Distinct implementations vary, but the common unifying property is the integration of spatially structured feature aggregation (using depth-wise convolutions, multi-scale convolutions, or frequency-aware processing) and channel-wise context weighting, with additional adaptations optimizing memory and computational requirements or targeting specific input modalities.

1. Architectural Principles

Conv-SE-NeXt blocks extend conventional convolutional units by introducing advanced feature aggregation and attention techniques. The canonical workflow comprises:

• Depth-wise or Multi-scale Convolution: Input features first pass through a spatial aggregation operation, often a depth-wise convolution, sometimes factorized into multi-branch or strip convolutions (as in SegNeXt (Guo et al., 2022)), or temporal variants for sequence data (as in NeXt-TDNN (Heo et al., 2023)).
• Channel Aggregation: Following spatial processing, a pointwise (1×1) convolution mixes channel information, resulting in richer embeddings and decreased parameter overhead.
• Squeeze-and-Excitation (SE) Module: Channel-wise global descriptors are computed via global pooling (e.g., $z_c = \frac{1}{HW}\sum_{i=1}^H\sum_{j=1}^W x_{ijc}$), then recalibrated with a gating function comprising one or two fully connected (or 1×1 convolutional) layers and a sigmoid or hardsigmoid activation (as in HARP-NeXt (Haidar et al., 8 Oct 2025) and SENet (Hu et al., 2017)). The resulting activations rescale feature channels, assigning context-dependent importances.
• Residual Connection: Many Conv-SE-NeXt blocks incorporate skip connections post-SE recalibration, fundamental for gradient flow and representation fidelity (Haidar et al., 8 Oct 2025).

A typical block for spatial data, as observed in HARP-NeXt (Haidar et al., 8 Oct 2025), performs: $$\begin{aligned} &y_{\text{dw}} = \sigma(\mathcal{N}(W_{\text{dw}} * x)), \ &y_{\text{pw}} = \mathcal{N}(W_{\text{pw}} * y_{\text{dw}}), \ &z_c = \frac{1}{HW} \sum_{i=1}^H \sum_{j=1}^W y_{\text{pw}}(i, j, c), \ &s_c = \sigma_2(W_2 \cdot \sigma_1(W_1 \cdot z_c)), \ &\tilde{y}_{c} = y_{\text{pw}} \odot s_c, \ &z = \tilde{y}_{c} + x \end{aligned}$$

2. Channel-Wise Recalibration and Attention Mechanism

The Squeeze-and-Excitation principle (Hu et al., 2017) is central to the block's discriminative role. Channel descriptors are computed via global pooling (squeeze), then passed through a bottlenecked FC or convolutional gating (excitation) that allows the network to model inter-channel dependencies and assign context-sensitive weights: $s = \sigma(W_2 \cdot \delta(W_1 \cdot z))$ with $W_1 \in \mathbb{R}^{C/r \times C}$, $W_2 \in \mathbb{R}^{C \times C/r}$, and $\delta$ a non-linearity (ReLU/GELU), $r$ a reduction ratio. The resultant scaling factors $s_c$ are broadcast-multiplied with the input or intermediate feature map channels. This mechanism is found to yield substantial accuracy gains with modest computational cost in image classification and segmentation, as well as sequence modeling applications (Hu et al., 2017, Boudouri et al., 14 Jan 2025, Haidar et al., 8 Oct 2025).

3. Multi-Scale and Multi-Branch Design

Modern Conv-SE-NeXt variants (e.g., SegNeXt (Guo et al., 2022), NeXt-TDNN (Heo et al., 2023), HARP-NeXt (Haidar et al., 8 Oct 2025), E-ConvNeXt (Wang et al., 28 Aug 2025)) frequently employ a multi-scale design. For instance:

• Spatial: Parallel branches with strip convolutions (e.g., $7 \times 1$, $1 \times 7$, $11 \times 1$, $1 \times 11$, $21 \times 1$, $1 \times 21$) approximate large receptive fields with linear cost, combined via summation and channel mixing (Guo et al., 2022).
• Temporal: Multi-scale temporal convolution in NeXt-TDNN, where parallel 1D depth-wise convolutions with varying kernel sizes model both short- and long-range dependencies (Heo et al., 2023).
• Hybrid/Hierarchical: In EMG-based gesture recognition (Shin et al., 4 Apr 2025), one branch (Stream-2) processes spatial-temporal features with separable convolutions and SE blocks, while others address long-term or bidirectional temporal structure, the outputs concatenated and then refined via channel attention.

These designs increase representational capacity and contextual awareness, which is particularly critical for segmentation, speaker verification, and multimodal or biomedical data.

4. Computational Efficiency and Structural Optimization

Conv-SE-NeXt blocks are engineered for efficiency:

• Depth-wise separable convolutions drastically reduce MAC operations and parameter count compared to regular convolutions (as utilized in HARP-NeXt (Haidar et al., 8 Oct 2025) and EMG gesture recognition (Shin et al., 4 Apr 2025)).
• CSPNet integration (E-ConvNeXt (Wang et al., 28 Aug 2025)): Stage-wise feature splitting, with partial propagation and recombination, reduces redundancy and network complexity by up to 80%. Use of stepped stems (series of small convolutions rather than one large stride) helps preserve fine spatial details.
• Normalization and activation: Batch Normalization (BN) replaces LayerNorm for speed, Hardswish and Hardsigmoid activations are preferred for embedded or real-time systems (Haidar et al., 8 Oct 2025). Fully connected layers in SE modules are often substituted with efficient 1×1 convolutions, particularly in E-ConvNeXt and HARP-NeXt variant SE blocks.

5. Discriminative Power and Noise Suppression

A critical insight from "On the Behavior of Convolutional Nets for Feature Extraction" (Garcia-Gasulla et al., 2017) is that all CNN features, both by their presence and absence, provide discriminative information. The paper quantifies feature separability using signed Kolmogorov–Smirnov statistics and Kullback–Leibler divergence between inner-class and outer-class activation distributions. Features with small separability scores are deemed noisy and can be pruned using thresholding: $$d_{\text{avg}}(x) = \frac{1}{|C|} \sum_{c \in C} |\{f : D_{KS}(f,c) > x \}| - \frac{1}{|C_{\text{rand}}|} \sum_{c' \in C_{\text{rand}}} |\{f : D_{KS}(f,c') > x \}|$$ with $t^+, t^-$ thresholds maximizing $d_{\text{avg}}(x)$. This statistical selection may be embedded as a gating module preceding SE recalibration, suppressing non-informative responses or universal activations.

6. Domain-Specific Adaptations and Practical Applications

Conv-SE-NeXt blocks demonstrate flexible adaptation across domains:

• 2D Vision: E.g., HARP-NeXt (Haidar et al., 8 Oct 2025) for range-image and point-based fusion in LiDAR segmentation, operating without deep stacking per stage and achieving competitive mIoU at sub-10 ms inference times.
• Speech Processing: Nextformer (Jiang et al., 2022) augments Conformer encoders with ConvNeXt-based time-frequency modules, improving character error rates with comparable FLOPs on AISHELL-1 and WenetSpeech.
• Biomedical Signals: In EMG-based gesture recognition (Shin et al., 4 Apr 2025), SE blocks enhance the differentiation of muscle signal patterns for prosthetic control.
• Emotion Recognition: EmoNeXt (Boudouri et al., 14 Jan 2025) leverages a spatial transformer entry module, ConvNeXt backbone, SE recalibration, and self-attention regularization for robust performance on FER2013.

7. Empirical Performance and Benchmarks

Conv-SE-NeXt blocks consistently improve recognition accuracy (classification, segmentation, or sequence transcription) without incurring substantial cost:

• SENet (SE block) (Hu et al., 2017): Top-5 ImageNet error reduced to 2.251%, a 25% improvement over prior state-of-the-art.
• HARP-NeXt (Haidar et al., 8 Oct 2025): Matches top-performing methods (PTv3) on nuScenes (77.1% mIoU) at $24\times$ speed.
• Nextformer (Jiang et al., 2022): SOTA on AISHELL-1 (4.06% CER) and WenetSpeech.
• EMG Gesture Recognition (Shin et al., 4 Apr 2025): >93% classification accuracy across Ninapro benchmarks.
• E-ConvNeXt (Wang et al., 28 Aug 2025): 78.3–81.9% ImageNet-1K Top-1 at <3.1 GFLOPs, competitive with much larger models.

A plausible implication is that the efficiency and discriminative strength of Conv-SE-NeXt blocks enable their deployment in time- and resource-constrained platforms (e.g., autonomous vehicles, wearable biosensors), as well as premiership in transfer and domain adaptation scenarios.

Summary Table: Key Implementation Variants

Variant / Paper	Domain	Distinguishing Features
HARP-NeXt (Haidar et al., 8 Oct 2025)	LiDAR Segmentation	Depth-wise separable conv, SE, residual, multi-scale fusion
Nextformer (Jiang et al., 2022)	Speech Recognition	Time-frequency ConvNeXt blocks, downsampling, LayerScale
SegNeXt (Guo et al., 2022)	Image Segmentation	Multi-branch strip convolutions, spatial attention
NeXt-TDNN (Heo et al., 2023)	Speaker Verification	Multi-scale 1D conv, Framewise FFN, GRN
EmoNeXt (Boudouri et al., 14 Jan 2025)	Emotion Recognition	STN, ConvNeXt, SE blocks, self-attention regularization
E-ConvNeXt (Wang et al., 28 Aug 2025)	Classification	CSPNet, stepped stem, BN, ESE attention
sEMG Gesture (Shin et al., 4 Apr 2025)	Biomedical Signals	Separable conv, SE, multi-stream fusion

In conclusion, the Conv-SE-NeXt Feature Extraction Block integrates spatially structured multi-scale convolutional processing with data-efficient channel-wise recalibration and, when necessary, discriminative thresholding and residual design. These principles, empirically validated across vision, speech, and biosignal domains, support both competitive accuracy and computational tractability, indicating strong suitability for high-impact, real-time AI systems.