Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Authors: Qi Xu
Junyang Zhu
Dongdong Zhou
Hao Chen
Yang Liu
Jiangrong Shen
Qiang Zhang
Venue: ICML 2025 poster (2025)
Keywords: spiking neural networks
brain-inspired computing
few-shot learning
Primary area: applications->neuroscience_cognitive_science
TL;DR: Deep spiking neural networks for efficient Few-shot Learning with self-cross features

Abstract: Deep neural networks (DNNs) excel in computer vision tasks, especially,  few-shot learning (FSL), which is increasingly important for generalizing from limited examples. However, DNNs are computationally expensive with scalability issues in real world. Spiking Neural Networks (SNNs), with their event-driven nature and low energy consumption, are particularly efficient in processing sparse and dynamic data, though they still encounter difficulties in capturing complex spatiotemporal features and performing accurate cross-class comparisons. To further enhance the performance and efficiency of SNNs in few-shot learning, we propose a few-shot learning framework based on SNNs, which combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption. We apply the combination of temporal efficient training loss and InfoNCE loss to optimize the temporal dynamics of spike trains and enhance the discriminative power. Experimental results show that the proposed FSL-SNN significantly improves the classification performance on the neuromorphic dataset N-Omniglot, and also achieves competitive performance to ANNs on static datasets such as CUB and miniImageNet with low power consumption.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Qi Xu'
Junyang Zhu'
Dongdong Zhou'
Hao Chen
Yang Liu'
Jiangrong Shen + 234 Qiang Zhang'
Abstract
1. Introduction
Deep neural networks (DNNs) (Krizhevsky et al., 2012: Szegedy et al., 2015; He et al., 2016) have demonstrated remarkable performance across various computer vision tasks, such as object detection and sematic segmentation. DNNs rely on large-scale labeled datasets and deep architec- tures, making them computationally expensive and energy- intensive, which limits their scalability. Real-world data is often scarce. dynamic, or con-stationary, which makes few-shot learning with low energy consumption increasingly important. Few-shot learning (FSL) (Fei-Fei et al., 2006; Vinyals et al., 2016) enables models to generalize from only few examples, which adapts to ever-changing conditions. Moreover, Spiking Neural Networks (SNNs), recognized as the third generation neural networks, are particularly well- suited to few-shot learning due to the exceptionally low energy consumption (Bu al., 2023; Ding al., 2022; Ma et al., 2024; Hu et al., 2023; Liu et al., 2024a;b; 2025b) compared to Artificial Neural Networks (ANNs) by lever- aging event-driven nature. Furthermore, the spatiotemporal dynamics of SNNs allow them effectively processing sparse and temporal data, which are common in open environments. This paper investigates the potential of SNNs enhance few-shot learning in these challenging circumstances, of- fering promising solution real-world applications that require both adaptability and efficiency.
Deep neural networks (DNNss excel in computer vision tasks, especially, few-shot learning (FSL), which is increasingly important for generalizing from limited examples. However, DNNs are com- putationally expensive with scalability issues in real world. Spiking Neural Networks (SNNs). with their event-driven nature and low energy consumption, are particularly efficient in pro- cessing sparse and dynamic data, though they still encounter difficulties in capturing complex spatiotemporal features and performing accurate cross-class comparisons. To further enhance the performance and efficiency of SNNs in few-shot learning, we propose a few-shot learning frame- work based on SNNs, which combines self- feature extractor module and cross-feature con- trastive module to refine feature representation and reduce power consumption. We apply the combination of temporal efficient training loss and InfoNCE loss to optimize the temporal dy- namics of spike trains and enhance the discrimi- native power. Experimental results show that the proposed FSL-SNN significantly improves the classification performance on the neuromorphic dataset N-Omniglot, and also achieves competi- tive performance to ANNs on static datasets such as CUB and minilmageNet with low power con- sumption.
Few-shot learning focuses on training models to address the challenge of learning with minimal labeled data. Typi- cal FSL approaches by ANNs rely on extensive parameter optimization (Finn al., 2017: Ravi & Larochelle, 2017;
Yoon et al., 2018) or metric learning (Qiao et al., 2019; Jiang al., 2020; Oreshkin et al., 2018), leveraging knowl- edge acquired during the training phase to classify new data points with few examples. However, a significant drawback of these ANN-based methods is their high computational cost and energy consumption. Since ANNS depend heavily on floating-point operations, they incur substantial energy costs, which can be prohibitive for deployment in energy- constrained facilities such as embedded systems Internet of Things (IoT) devices (Madakam et al., 2015; Guo et al., 2023b; Liu et al., 2024b). Additionally, the reliance on large datasets and backpropagation algorithms (Cilimkovic, 2015)
'School of Computer Science and Technology, Dalian Univer- sity of Technology.Dalin Dalian, China FFacliyy Electronic and In- formation Engineering. Xi' an Jiaotong University 'National Key Lab of Human-Machine Hybrid Augmented Intelligence. Xi' an Jiaotong University SStte Key Lab of Brain-Machine Intelligence, Zhejiang University. Correspondence to: Jiangrong Shen <jr- shen @ ed..'....n>>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
for effective learning limits the generalization capability in few-shot learning scenarios.
experiments on static and neuromorphic datasets. Par- ticularly, we the first report an accuracy of 98.9% on 5w5s on N-Omniglot and achieve great performance close to ANNs on CUB and minilmageNet dataset, which sets a new state-of-the-art performance of SNN- based methods.
To address these issues, researchers have turned to SNNs (Maass, 1997; Cao et al., 2015; Sorbaro et al., 2020; Guo et al., 2023a; Guan et al., 2023; Xu et al., 2023; 2024a; Shen et al., 2025; 2024), which emulate the spiking mechanism of biological neurons and offer the potential for significant reductions in computational cost and energy consumption. By encoding information through discrete spike trains and performing computations in an event-driven manner, SNNs exhibit an inherent ad advantage in energy efficiency (Xu et al., 2024b), making them promising candidates for resource- constrained applications. Recent studies have started to explore SNNs for few-shot learning by utilizing spike-time encoding and spike sequence processing to enable learning from limited data. However, existing ENN-based few-shot learning methods still face several challenges that need to be addressed. Firstly, SNNs often struggle to effectively capture features from complex spatiotemporal features of inputs, which limits their capacity to extract rich image features. Secondly, current approaches lack of cross-class feature comparison and classification, making it difficult to accurately map query samples to the appropriate support sets.
2. Related Work
Few-shot learning refers to the issue of training models to generalize from a limited number of examples. Unlike traditional machine learning tasks, where large datasets with thousands of labeled examples are available for training, FSL aims to train a model to recognize novel classes using only few labeled instances. This challenge is particularly relevant in scenarios where acquiring a large amount of labeled data is impractical or expensive, such as medical image analysis, where annotations require expert knowledge, or personalized applications, where user-specific data is scarce.
ANN FSL Recent studies have demonstrated various few- shot learning methods utilizing ANNs. For instance, QS- Former (Wang et al., 2023) enhances few-shot classifica- tion performance through a unified Query-Support Trans- former architecture, effectively integrating global and local feature extraction. Similarly, DT-FSL (Ran et al., 2023) merges deep Transformer models with meta-learning strate- gies to improve hyperspectral image classification, apply- ing domain adaptation techniques to mitigate distribution discrepancies. CTFSL (Peng et al., 2023) combines convo- lutional neural networks (CNNs) with Vision Transformers to achieve cross-domain hyperspectral image classification, enhancing generalization through domain alignment and dis- crimination. Furthermore, Luo (Luo et al., 2023) suggests that the independence of training and adaptation algorithms can simplify the development of few-shot learning tech- niques. A polyp classification system (Krenzer et al., 2023) based on deep learning has also shown improved diagnos- tic accuracy by leveraging few-shot and deep metric learn- ing. Furthermore, EASY (Bendou et al., 2022) employs network and Y-shaped structure in train- ing, achieving advanced few-shot classification performance across multiple standard datasets. These studies collectively illustrate that ANNs could achieve good performance in few-shot learning.
Given these challenges, this paper introduces a novel net- work to leverage SNNs for spatiotemporal feature extraction in few-shot learning tasks. Our approach integrates a self- feature extractor module and a cross-feature contrastive module to further refine feature representation and classify the query set into specific support set. The proposed method achieves a significant advancement by combining the energy- -ffrgieefficintt SNN with an innovative feature extrac- tion mechanism, thereby enhancing feature representation capability and classification accuracy in few-shot learning. By addressing the high energy consumption of traditional ANN approaches and improving the performance of SNNs in few-shot scenarios, our approach achieves both outrang- ing performance and practical significance. Our contribu- tions are summarized as follows:
This paper proposes few-shot learning framework based on spiking neural networks, which combines self-feature extractor module and cross- feature con- trastive module to further refine feature representation and greatly reduce power consumption.
SNN FSL Recent studies highlight the wide application and potential of SNNs in few-shot learning. Efficient online few-shot learning (Stewart et al., 2020a) has been applied to the Intel Loihi neuromorphic processor through proxy gradient descent and transfer learning for gesture recogni- tion. Based on this, the natural e-prop method is proposed to facilitate she-shot learning with the learning signals emit- ted by recursive SNNs, aligning closely with biological
By combining TET Loss and InfoNCE Loss, our model shows strong generalization ability and noise resis- tance. TET Loss reduces computational overhead and speeds up training, while InfoNCE Loss improves the model's ability to distinguish between positive and negative samples, enhancing robustness to noisy data.
We fully demonstrate the model's 's effectiveness through
2

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
learning mechanisms and allowing rapid adaptation to new tasks (Scherr al., 2020). Furthermore, the development of SOEL system (Stewart et al., 2020b), which integrates proxy gradient descent with trire-riggeeed learning, has significantly improved the efficiency of online learning on neuromorphic hardwares. Jiang (Jiang et al., 2021) proposes a multi-timescale optimization framework, introducing an adaptive-gate gated LSTM to balance short-term learning with long-term evolution acquire prior knowledge through example-level learning and task-level optimization. Stewart proposes a method that combines of meta-learning (MAMI.) and proxy gradient (Stewart& & Neftci, 2022) to improve the rapid learning capabilities of SNN, making it particularly suitable for low-power and fast adaptation scenarios. HES- FOL (Yang et al., 2022) boosts the robustness and accuracy of SNNs in non-Gaussian noise through heterogeneous integrated loss function. Complementing this research, bionic SNN designed for gas recognition (Huo et al., 2023) supports the incremental learning of few-shot classes while effectively addressing sensor drift and aging issues. These studies demonstrate that SNNs could achieve both efficient performance and strong adaptability in few-shot learning.
3.2. Few-shot Classification
Few-shot classification aims to solve the problem of effec- tively classifying new samples when there only few labeled samples in each category. Due to the extremely limited labeled data, traditional deep learning methods are prone to overfit in this case and fail to unseen data. To resolve this issue, few-shot learning usually adopts a meta- learning framework and improves the adaptability through episodic training.
In few-shot classification, FSL is usually defined as N-way K-shot task (i.e., K labeled samples of N unique classes) and K is very small, e.g., 1 or 5. The model is initially trained on training data Drrain from a set of training classes Crrain. and then evaluated on test data Desst from unseen classes Cuest where Carain n Cisst = Ø, which is the most impor- tant setting for few-shot learning. Both Dusin and Diest consist of multiple episodes, each containing query set and support set S {(zi, y:) ) X of K image-label pairs for each of the N classes, also known as an N-way K-shot episode. During training, we iteratively sample an episode from Dtrain and train the model to learn a mapping from the support set S and query images I to the corresponding query labels yg. This process involves learning how to generalize from the limited information pro- vided by the support set to predict accurately on the query set. During testing, the model uses the learned mapping to classify query images lg as one of the N classes in the support set S sampled from Drest- The goal is to evaluate the model's 's ability to generalize to new, unseen classes with limited data.
Q = {( Mj م x
3. Preliminary
3.1. Spiking neuron model
Leaky integrate-and-Fing (LIF) (Shamsi et al., 2017) is the most commonly used neuron model in SNNs. It captures the dynamic characteristics of spikes by simulating the behavior of biological neurons. The LIF model mimies how neurons work and generate spikes with membrane potential and a certain threshold. The whole process can be described as:
4. Methodology
- ())=)
(1) In this section, we will introduce our network model ar- chitecture, which uses a spiking neural network to extract (2) image features. The self-feature extractor module and the cross-feature contrastive module further extract features and classify the query set into a certain support set. The entire network model architecture is shown in the figure 1. We first briefly introduce the whole model structure, then specif- ically introduce the technical details of each module, and finally explain our specific training strategy.
S(t) = O(U(()- Vin),
U(I) = U()) (1 S()),
(3)
where 7 is a constant leaky factor, (t) is the membrane potential at time t, X (t) is the input, and θ represents the Hcaviside step function. Given a certain threshold Voa. if (t) exceeds this threshold, the neuron generates a spike, then (1) is reset to O. The firing function and hard reset mechanism can be described as: when U (t)2Vsth the neu- ron generates a spike, and then (t) is reset to and enters recovery period until the membrane potential rises to the threshold again; when U() reaches certain threshold Veh. the output spike St) is triggered by the step function. LIF neurons are often used in SNNs to replace the activation function in traditional ANNs with simulating the behavior of biological neurons.
4.1. Model Architecture
First, support set images spt and query set images qry are sent to the spiking network backbone to extract the basic fea- ture representation, where the weights of the two are shared, and then enter the self-feature eXtractor(SFE) module to analyze the internal correlation of the image, and finally enter the cross-feature CC)F module to elassify the query set into the nearest support set. We named our model SSCF (Spiking Self-Cross Feature Network).
3

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Spiking Backbome
Self-featuro extractor
spt
X
Conyt C
ComnC-C)
Cross-fcature contrast
(LC.WW))
shared
(T.CHH,W)
Conv(1-C)
Conv(C
Comv4D
m Linfo
Spiking
Backhone
Self-feuture exiractor
gry
Con Ch
Cear .c]
Coar(C
ConolN
(I..I.W
FC
LIET
Figure 1. Architecture of the proposed few-shot learning framework based on SNNs. It combines self-feature extractor module and cross-fcature contrastive modulc to further refine feature representation and greatly reduce power consumption.
4.2. Backbone
correlation to retain the rich semantics of the feature vector for classification, which suppresses changes in appearance and reveals structural patterns.
The backbone network we use is VGGSNN, specifically, the spiking form VGGI6, a total of 8 conv-bn-LIF layers. Note that the backbone we use is relatively simple because an overly complex backbone is prone to overfitting, which is not conducive to the generalization ability of few-shot learn- ing, We did not apply the average pooling layer since the average pooling operation would lose spatial information. A key feature of SNN is that they can operate sparsely, in other words, neurons will only generate spikes when the input stimulus exceeds a certain threshold. Avoiding the use of average pooling could help maintain this sparsity and may be more efficient in hardware implementation and reduce unnecessary calculations. For the input format, replicate the static dataset 7' time steps to meet the input requirements of our spiking backbone. For the neural morphology dataset, due to its natural T-dimension, there no need to perform excessive operations. We will temporarily refer to the pre- liminary features obtained through the spiking backbone network as Fo.
Then, we apply a convolutional block that follows the com- putational efficiency bottleneck structure obtain the self- correlation pattern in F,, namely I x convolution layer for reducing the number of channels and two 3x3 convolu- tion layers for transformation, and a 1x convolution layer to restore the size of the number of channels. This series of convolution operations gradually aggregates local corre- lation patterns without padding, reducing the dimension of U x V x 1. After that, the LIF neuron layer is inserted as the activation function. For the entire SFE module, the feature dimension remains unchanged as T x C x H x W. This feature extraction is complementary to the feature F obtained after the spiking backbone, so we combine the representation of the two modes, applying the residual struc- ture so that the features sent to the CFC module are the sum of the two, which strengthens the representation of basic features with relational features and helps few-shot learning better understand "observe yourself for what" in the image:
4.3. Self Feature (Xtractor(SFE)
Fo +
(4)
The SFE module pays more attention to the information inside the image and provides a reliable input for the CFC module. Given feature representation Fo x where T is the timestep, C' is the channel, and H x W de- notes the spatial resolution. First, we use the unfolded operation to make each position x E [1, H) x [1, W] in T x C expanded U and V dimension. Hence, turns to Fo E XXXXxHXWXUXV We use time-channel self-
This approach helps in recognizing intra-class features and facilitates generalization to unseen target categorics.
4.4. Cross Feature Contrastive(CFC)
The CFC module takes the input pairs F, and F of the support set and the query set and generates the correspond- ing attention maps Aq and A.. We first take the average

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
value of the T dimension to facilitate subsequent operations. Then, the query and support representations Fg and F, are converted into more compact representations using point- wise convolutional layers, reducing the channel dimension C' to C². We construct four-dimensional cross-correlation tensor C E " H w Since the dimensions // and W have been reduced after the convolutional layers of the spik- ing backbone, the memory usage of the cross-oorcelation tensor is not particularly large. However, due to some large appearance changes in the few-shot learning setting, we adopt convolutional matching process. The tensor is fur- ther refined using 4D convolutions with matching kernels, specifically consisting of two 4D convolutional layers. The first convolution generates multiple correlation tensors with multiple matching kernels, increasing the number of chan- nels to C and the second convolution aggregates them into a single 4D correloornll tensor. From the refined cross-correlation tensor C, we generate joint attention maps Ag and As, which reveal the relevant content between the query set and the support set. The attention map for query Aq E IR HXW is computed by (Kang et al., 2021)
TET loss isenng al., 2022):
1 LTET - Σ ():
(6)
where T is the time step, LOE represents the cross-entropy loss, and y is the sample label.
Next, we use the pooling to obtain the final feature expres- sion based on the contrast metric loss. The support set is first divided into positive and negative classes according to the labels so that the network can better learn the correct category, and then the following contrast loss is calculated:
= Q = AgAg:
LIIONCE = - llog exp(sim( (s+, Q)/T)
(8)
Σ exppssim(S Q)/T)*
where sim(:.-) is cosine similarity and T is scalar temper- ature factor. At inference process, the class of the query is predicted as that of the nearest prototype. The final loss function combines these two losses, where À is a hyper- parameter that balances the loss terms:
Ax(xa)= softmax(CC (Cxxg x,)/7)
(5)
HW
LTttal = >LTETT + (1 - Cin
(9)
where x is the position on the feature map and is the temperature factor. The attention value (x(x)) can be interpreted as the ratio of the matching score of Xq. as the
5. Experiments
average probability of matching the position at the query image with the position the support image. Similarly, the attention map for the support is computed by switching the query and support in equation 5. These joint attention maps improve the accuracy of few-shot classification by cross- correlating patterns and adjusting "important locations of joint attention" with respect to the image given at the test time.
5.1. Datasets
We adopt the following datasets for experiments: N- Omniglot, CUB-200-201,, and minilmageNet. Meanwhile, as a few-shot learning study, we must ensure that the train- ing set and the test set are different, that is, their intersection is empty.
N-Omniglot(Li et al., 2022) a neuromorphic dataset built based on the original Omniglot dataset, which consists of 1623 handwritten characters from 50 different languages. Each character has only 20 different samples.
4.5. Training Losses
We train the network in a single-stage way, combining two losses to guide the model to classify precisely: TET- based loss and contrast-based loss. First, we append fully connected classification layer after F calculate LrET, which guides the model to correctly classify queries of class E Cirain- The contrast-based metric loss Linto calculates the cosine similarity between the query and the support prototype embeddings, separates the positive and negative classes, and finally calculates the infoNCE loss to map the query embedding to the support embedding of the same class. When inferencing, the query class is predicted to be the class of the closest support set.
CUB-200-2011 is a dataset focused on the fine-grained clas- sification of birds and is widely used in the field of few-shot learning. has a total of 200 categories, of which 100, 50, and 50 are used for training, validation, and testing, respec- tively.
mini ImieelmaeeNet(We (Vinyals et al., 2016) is derived dataset from ImageNet that comprises a total of 60,000 images, which are evenly distributed across 100 different object cat- egories. Of these categories, 64 are designated for training, 16 for validation, and 20 are reserved for testing.
5.2. Experimental Results
Since SNN has an additional time dimension than ANNs, our learning goal should be reconsidered. We use TET loss to train our backbone network. It turns out that TET loss effective for spiking neural networks. The calculation of
We conducted experiments on the N-Omniglot dataset under different time steps and scenario settings for performance evaluation. Experimental results are shown in Table 1. For
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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
method
backbone
20w1s
20w5s
5w1s
5w5s
13.3+1.6
74.9+1.6
72.6+0.9
88.4+1.0
Siamese(Koch et al., 2015)
SCNN
8
50.8+0.4
72.2+0.8
74.4=0.7
90.7+0.1
12
1..
71.3+1.0
69.3#0.8
85.7+0.6
4
74.4=0.7
90.7+0.1
MAML(Finn et al., 2017)
SCNN
8
71.1
88.6+0.4
12
70.3+0.9
87.3+0.3
4
67.020.8
79.0=0.7
79.3+0.7
95.0+0.6
SSCF(ours)
SCNN
8
67.640.6
80.2+0.6
79.5
95.5+0.2
67.8+0.5 12
79.7=0.5
80.0#0.3
96.2+0.4
4
83.720.7
94.820.3
94.3+0.8
98.6+0.3
SSCF(ours)
VGGSNN
8
84.2+0.6
94.7=0.3
94.8=0.7
98.9+0.3
12
84.4+0.6
94.920.2
95.3+0.6
98.8+0.2
Table 1. Performance comparison among different SNNs-based FSL models on the N-Omniglot dataset
the convenience of comparison, we conducted experiments on both the SCNN backbone network and the VGGSNN backbone network. The experimental results on the SCNN backhone network surpassed maml and siamese, proving the effectiveness of our model architecture. The experimental results on the VGGSNN backbone network were further improved, which proved the power of VGGSNN ing neural backbone network. We can observe that when the number of ways remains constant, increasing the number of shots leads to a higher classification accuracy. When the number of shots remains constant, reducing the number of ways results in higher accuracy. In the 1-shot scenario, as the time step T increases, the accuracy consistently im- proves. However, with increase in the number of shots, such as in our 5-shot experiments, the impact of the time step T' on accuracy becomes minimal. This is because, in the 1 1-shot scenario, the limited number of samples means that a longer time step can provide more information to the model, thereby enhancing its performance. In the 5-way I-shot (5w1s) setting, our model achieves an accuracy of 95.3% at T=12, representing 25.0% improvement over MAML. In the 5-way 5-shot (5w5s) setting, the performance of our model is relatively stable across different time steps, with the accuracy consistently around 98.8%, which is 1 11.5% improvement over MAML. In the 20-way 1-shot (20wls) and 20-way 5-shot (20w5s) settings, our model achieves ac- curacies of 84.4% and 94.2%, respectively, representing im- provements of 34.6% and 23.6% over the Siamese method reported in the original paper. Results show particularly sig- nificant improvements in the 1-shot setting, which further proves its strong generalization performance and suitabil- ity for solving few-shot learning problems. These results clearly demonstrate that our model performs exceptionally well on neuromorphic datasets. In figure 2, we visualize the t-SNE graph different time steps and different ways.
We perform experiments on the CUB and minilmageNet datasets. Results are shown in Table 2 and Table 3.On the CUB dataset, our model achieves an accuracy of 76.27% in the 5-way 1-shot (5w1s) setting and 87 $7.00% in the way 5-shot (5w5s) setting. On the minilmageNet dataset, our model achieves an accuracy of 60.97% in the 5w1s wls set- ting and 75.61% in the 5w5s setting. These results surpass many classic ANNs methods and are close to the current state-of ootte-of-the-art performance of ANNs.
spik-
We found that, since the CUB dataset is a line-grained im- age dataset, balancing the loss terms favors self-correlation features. Given that all images in the dataset are of birds, inter-class features are less important. This does not mean that inter-class feature comparison can be omitted; both types of features still need to be appropriately balanced. The minilmageNNet dataset, on the other hand, contains many classes with significant differences, making cross-class fea- tures more important than the CUB dataset. However, the optimal À coefficient is still greater than 0.5, as inter-class features are derived from self-class features. Table 4 shows our experimental results with different values of X, which supports the above observations.
5.3. Ablation Studies
Table 5 presents the results of our ablation study, which is conducted on the N-Omniglot and CUB datasets. Results show that both modules are beneficial to the model, with the SFE "Self-Feature Extraction) module being particularly crucial for improving the performance. Specifically, on the N- N-Omniglot dataset, removing the SFE module leads to a significant drop in accuracy, from 94.34% 93. in the 5 w-w 1-shot (5w1s) setting. Similarly, on the CUB dataset, the removal of the SFE module causes a substantial decrease in accuracy, from 76.27% to 71.84% in the 5wls setting. This indicates that the SFE module is essential for capturing
Next, we evaluate the performance static datasets.
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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
method
backhone
5-way I-shot
5-way 5-shot
ProtoNet(Snell et al., 2017)
ResNet12
66,0940.92
82.50=0,58
RelationNet(Sung et al., 2018)
ResNet34
66.20#0.99
82.30=0.58
DEML+ ---Meta-SGDZZND et al., 2018)
ResNet50
66.95+1.06
77.11=0,78
MAML(Finn al., 2017)
ResNet34
67.28
83.47=0.59
& Rose, 2022)
MobileNetV2
72.90=0.24
81.76=0.25
S2M2(Mangla et al., 2020)
ResNet34
72.92+0.83
86.55=0.51
FEAT(YYe et al., 2020)
ResNet12
73.27+0.22
85.77=0.14
MergeNeet-MAAXAAtabboo Rose, 2022)
EfficientNetBOO
75.3440.21
83.4220.29
DeepEMID(Zhang et al., 2020)
ResNet12
75.6540.83
58.6920.50
RENet(Kang al., 2021)
ResNet12
79.4920.44
91.1120.24
SSCF(ours)
VGGSNN
76.2740.46
87.00=0.30
Table Performance comparison on CUB dataset
method
backbone
5-way I-shot
5-way 5-shot
MAML(Finn et al., 2017)
ResNet34
48.70=1.84
63.11
Meta-SGD(ZZou et al., 2018)
ResNet50
50.47:1.87
64.66+00.9
ada dedeeeNe(MMunkhlllaa et al., 2018)
ResNet12
56.88=0.62
71.94+0.57
RelationNet(Sung et al., 2018)
ResNet34
57.02=0.92
71.0740.69
Dual TriNet(Chen et al.. 2019)
ResNet18
58.12=1.37
76.92+0.69
ProtoNet(Snell et al., 2017)
ResNet12
62.39+0.21
80.5340.15
S2M2(Mangla et al., 2020)
ResNet34
63.7420.18
79.45+0.15 12
DeepEMDDZZann et al., 2020)
ResNet12
65.91=0.82
82.4140.56
RENet(Kang et al., 2021)
ResNet12
67.6020.44
82.58+0.30
SSCF(ours)
VGGSNN
60.97=0.45
70.61+0.34
Table 3. Performance comparison minilmageNet dataset
0.2
0.4
0.6
0.7
0.8
1.0
noise
0.0
0.4
0.8
45.98
66.67
73.56
76.27
72.17
68.37
CE(Wang et al., 2019)
55.135
40.649
32.725
mini
35.89
50.02
60.97
60.71
60.74
53.19
iCEoN et al., 2020)
55.302
43.699
36.485
Table Performance comparison under different on CUB and minilmageNet with time steps T=2
Table 6. Performance under different levels of noise on CUB
SFE
CFC
N-Omniglot
CUB
evitable noises. To address this issue, we modified the data preprocessing steps and introduced Gaussian noise to the dataset. Specifically, we selected the bird dataset CUB and added noise rates of 0.0, 0.4, and 0.8 for our experiments. The comparison results, shown in Table demonstrate that using the infoNCE loss function yields better performance compared to using only the ordinary cross-entropy (CE) loss. At noise rate of 0.0, the model using infoNCE achieves an accuracy of 55.302%, while the model using CE achieved 55. 135%. As the noise rate increases to 0.4, the infoNCE model maintains a higher accuracy of 43.699%, whereas the CE model's s accuracy drops to 40.649%. Even at the highest noise rate of 0.8. the infoNCE model outperforms the CE model with accuracy of 36.485% compared to 32.725%. These results clearly indicate that our model, when using the infoNCE loss, exhibits superior robustness and is better equipped to handle noisy data, which a common issue in few-shot learning scenarios.
92.13
69.97
X
94.07(+1.94)
72.20(+2.23)
x
93.17(+1.04)
71.84(+1.87) 94.34(+2.21)
76.27(+6.30)
Table 5. Ablation studies SFE and CFC components
fine-graine grained details and improving classification accuracy, especially in datasets with closely related classes. While the other module also contributes to the model's performance, its impact is less pronouned compared to the SFE module. These findings highlight the importance of both modules and demonstrate the effectiveness of our model in handling both fine-grained and general classification tasks.
The primary challenge in few-shot learning is the lack of rich and diverse data. For instance, in identifying rare an- imal species and rare diseases, the environments in which we collect data are often complex and difficult, leading to in-
Our model has significant advantages over traditional ANN

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
5w1s
20w Is
A
T=4
T=8
T=12
Figure 2. T-SNE visualization on N-Omniglot in different time steps. We can intuitively see the clustering effect in the feature space and the impact the time step on the feature distribution. shorter time step (such as T=4), feature distribution more dispersed the distinction between categories is low. As the time step increases (such as T=12), the feature distribution gradually becomes more compact and the distinction between categories is significantly improved.
6. Conclusion
Method
acc
SOP
FLOPs
Energy
This paper mainly focuses implementing few-shot learn- ing with SNN to alleviate the issue of insufficient general- ization ability and improve the energy efficiency, We com- bine self-feature extractor module and a cross-feature con- trastive module to further refine feature representation and greatly reduce power consumption. Our training strategy combines TET loss and infoNCE loss at the same time. This method has good performance the neuromorphic dataset N-Omniglot and also obtains competitive performance with ANNs on static datasets such as CUB and minilmageNet. The energy estimation also shows the effiency priority of our model.
ReNet
79.49
4.84G
22.264mJ
ours
76.27
1.39G
0.13G
1.849mJ
Table Comparison of the energy consumption with ANNs
in terms of deploying on neuromorphic hardware. Next, will perform theoretical synaptic operation and energy consumption calculation (Liu et al., 2025a). For SNNs, we must first calculate the synaptic operation and then calcu- late the energy consumption. The energy consumption is proportional to the number of operations. The calculation is:
Thus, advancing FSL research not only addresses critical limitations in current AI methodologies but also meets the growing demand for adaptive, efficient, and scalable solu- tions in real-world applications. Future work will focus on further refining the model to improve its performance on even more challenging datasets and tasks. We aim to explore advanced techniques for feature extraction and loss function optimization to enhance the model's generalization ability. Additionally, plan to investigate the integration of additional spiking attention- based mechanisms and tem- poral dynamics to better capture the temporal aspects of data, which are often crucial in real-world applications.
SOPs = fr x T x FLOPs,
(10)
ESNN = 0.9pJ x SOPs,
(11)
FANN 4.6pJ x FL.OSs.
(12)
where fr is the firing rate of the input spike train of the block/layer and the simulation time step of spike neu- ron. 0.9pJ is the energy consumption each SOP(Hu et al.,
2021; Indiveri al., 2015). For ANNs, 4.6pJ is the en- ergy consumption of each FLOP. From Table 7, we can see that experiment is compared with the ANNs method. When T=2, the energy consumption of our method is only 8.30% of the ANNs'. This proves that SNN is particularly good in power consumption optimization with its unique event-driven computing method and sparse data processing capabilities.
8

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Acknowledgements
Fei-Fei, L.., Fergus, R., and Perona, P. One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4):594-611, 2006.
This work was supported by the National Natural Science Foundation of China under Grant (No. 62306274, 62476035, 62206037, 61925603, U24B20140), and in part by the Young Elite Scientists Sponsorship Program by CAST un- der Grant 2024QNRC001. Open Research Program of the National Key Laboratory of Brain-Machine Intelligence, Zhejiang University (No. BMI2400012).
Finn, C., Abbeel, P., and Levine, S. Model-agnostic meta- learning for fast adaptation of deep networks. In Interna- tional conference on machine learning, pp. 1126-11355 PMLR, 2017.
Guan, B., Zhao, J., Barath, D., and Fraundorfer, F. Minimal solvers for relative pose estimation of multi-camera sys- tems using affine correspondences. International Journal of Computer Vision, (31(1):324 -45324444,5,10 2023.
Impact Statement
This paper presents work whose goal is advance the field of Deep Learning. There are many potential societal conse- quences of our work, none which we feel must be specifi- cally highlighted here.
Guo, J., Xu, D., and Lu, G. Chanet: Towards complexity and bitrate adaptive deep image compression using a single network. IEEE Transactions on Image Processing, 2023a.
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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
A. Supplementary Materials
In order to gain a deeper understanding of the encoder's performance during feature extraction, we extracted the encoder part of the model and added spike visualization module intuitively display the spike activity in the spiking neural network (SNN). With this method, can clearly observe the activation patterns and their changing patterns of spiking neurons at different time steps. The experimental results show that within the initial time step, the spike activity is mainly focused on capturing the key features of the input data, showing the encoder's high sensitivity to important information. As the time step increases, more and more features are activated and emit spikes, indicating that the encoder can gradually identify more subtle features. This phenomenon reveals that the encoder not only maintains sensitivity to the main features at long time steps, but also gradually captures more complex detail features, thereby enhancing the overall performance of the model. The introduction of spike visualization not only helps us better understand the internal working mechanism of the encoder, but also provides valuable insights for further optimizing the model.
5091
T=1
T=2
Figure 3. Visualization spiking activities at different time steps.In the above picture, we can clearly see changes in the activity of the spiking. The features at time T=2 are significantly more than those at time T=1. This shows that the time step increases, more and more features are activated and spikings are emitted, thereby capturing more features.
In order to further explore the performance of spiking neural networks (SNNs) static datasets, conducted experiments with different time steps on two commonly used static datasets, CUB and minilmageNee, and used pulse visualization techniques to show the changes in pulse activity. The experimental results show that within the initial time step, the encoder mainly captures the key features of the input data, showing high sensitivity to important information. As the time step increases, more and more features are activated and emit pulses, indicating that the encoder can gradually identify more subtle features. Specifically, on the CUB dataset, as the time step increases from 2 and the model's ability to capture complex features is significantly improved, and the classification accuracy also increases accordingly. Similarly, the minilmageNet dataset, longer time step enables the model to better process detailed features in the image, thereby improving the overall classification performance.
12

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning
Dataset
acc T
71.43:10.48
CUB
73.27=:0.47
76.42-+-0.66
60.26:t:.45
mindImageNet
60.43::0.46
60.75=0.57
Table 8. Accuracy under different 7 values on CUB and minilmageNet datasets.
In this study, we further explored the impact different levels of Gaussian noise on model performance, especially comparing the performance of the InfoNCE loss function with the cross entropy (CE) loss function on neuromorphic datasets and static datasets. Experimental results show that under various noise conditions, whether it is neuromorphic dataset static dataset, the InfoNCE loss function exhibits superior noise resistance compared to the traditional cross entropy loss function. As the noise level increases, the InfoNCE loss funetion not only maintains high classification accuracy, but its advantage over the CE loss function also becomes more obvious. These results show that the InfoNCE loss function is significantly more robust than traditional methods in dealing with high noise environments, and exhibits stronger noise resistance when the noise level increases, with higher reliability and applicability.
Dataset
Noise
Loss
Acc
CE
93,8
0.0
InfoNCE
94.3
CE
84.2
N-Omniglot
0.4
InfoNCE
86.5
CE
75.6
0.8
InfoNCE
79.4
CE
74.1
0.0
InfoNCE
77.4
CE 59.8
CUB
0.4
InfoNCE 65.7
CE 36.5
0.8
InfoNCE 43.6
Table Performance InfoNCE and cross entropy loss functions on different datasets different noise levels
We have also added some of the latest SNN few-shot learning methods for comparison, The performance comparison show as follows:
Dataset
method
backbone
Task
acc
plain
SCNN
63.4
Showledge-Transfer(He SSCF(ours)
Transfer(He et al., 2024)
SCNN
64.1
N-Omniglot
20-way 1-shot
SCNN
67.8
SSCF(ours)
VGGSNN
84.4
Table Performance comparison recent SNN methods on N-Omniglot dataset under 20-way 1-shot classification task
Dataset
method
backbone
Task
acc
OWOML (Rosenfeld al., 2021)
SNN-ResNet 12
45,2
CESM (Zhan al., 2024)
SNN- MIN-WiidRResNet225 10
51.8
minilmageNet
5-way 1-shot
MESM(Zhan al., 2024)
SNN-W MIN-WiidReesNe-225 20-10
53.6
SSCF(ours)
VGGSNN
60.9
Table 11. Performance comparison of recent SNN methods on minilmageNet datasct for 5-way 1-shot classification
13


Reviews:
[{"review_id": "1CjG7bpyl7", "reviewer": "Reviewer_VAei", "summary": "This paper focuses on leveraging spiking neural networks (SNNs) for few-shot learning (FSL) to enhance generalization ability and energy efficiency. The proposed method combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption. Experimental results show that the proposed method improves the classification performance with low power consumption.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the insightful feedback, which has helped us clarify our contributions and identify areas for improvement.\n\n**Q1:How to tackles the challenges of SNN’s few shot learning? Simply adds two simple attention-like modules ?**\n\nR1:We appreciate this critique and clarify that our key innovations go beyond simple \"attention-like\" modules: Our innovations transcend conventional attention mechanisms: The Self-feature extractor enhances intra-class representation through relational feature integration (\"observe yourself for what\" ); The Cross Feature Contrastive module dynamically models support-query relationships via joint attention maps. Together, they synergistically improve SNN few-shot learning through dual optimization of self-representation and cross-class comparison, not mere attention replication.\n\n**Q2: Cross-class comparison?**\n\nR2:Our method enables cross-class comparison via two specialized modules: A bottleneck-structured self-feature extractor capturing intra-class patterns while boosting inter-class discrimination; A 4D-convolution based cross-feature comparator generating attention maps for explicit query-support relationship modeling. This SNN-compatible design combines event-driven LIF dynamics with advanced feature extraction, achieving 98.9% accuracy (5-way 5-shot on N-Omniglot) with clear feature separation (t-SNE verified).\n\n**Q3:Data scarcity**\n\nR3:Data scarcity drives few-shot learning research, motivating our development of SNN-based solutions that leverage meta-learning and metric learning to extract meaningful patterns from limited samples while maintaining generalization capability. Our systematic validation demonstrates significant improvements through: comprehensive benchmarking against state-of-the-art SNN few-shot methods (see R4 results), and synergistic operation of our novel self-feature extraction and cross-feature comparison modules, which collectively enhance few-shot learning performance in SNNs while preserving their energy-efficient characteristics.\n\n**Q4:Compared to other recent advanced SNNs’ few shot learning methods.**\n\nR4:Thanks for your suggestion.We have added some of the latest SNN few-shot learning methods for comparison, The performance comparison is show as follows:\n| Dataset    | method              | backbone | Task         | acc  |\n|------------|---------------------|----------|--------------|------|\n| **N-Omniglot** | plain[1]           | SCNN     | 20-way 1-shot | 63.4 |\n|            | Knowledge-Transfer[2] | SCNN     |              | 64.1 |\n|            | SSCF(ours)          | SCNN     |              | 67.8 |\n|            | SSCF(ours)          | VGGSNN   |              | 84.4 |\n\n| Dataset          | method      | backbone               | Task         | acc  |\n|------------------|-------------|------------------------|--------------|------|\n| *mini*ImageNet  | OWOML [3]   | SNN-ResNet-12          | 5-way 1-shot | 45.2 |\n|                  | CESM [4]    | SNN-WideResNet-28-10   |              | 51.8 |\n|                  | MESM [4]    | SNN-WideResNet-28-10   |              | 53.6 |\n|                  | SSCF(ours)  | VGGSNN                 |              | 60.9 |\n\n**Q5:The Generalization ability of the proposed model.**\n\nThanks for your suggestion. Here we validate the performance of our model on N-Omniglot, CUB, and *mini*ImageNet datasets, which are common-used validation settings as [5-7] . These datasets are recognized benchmark datasets in the FSL field, covering neuromorphic data, fine-grained classification, and general object classification scenarios, respectively.Meanwhile, in order to further verify the generalization performance and robustness of the model, we supplemented the performance of the model under different datasets and different noise levels as  Table 1 in Reviewer 4's comments.\n\nThanks for your feedback. We have re-uploaded high-resolution images in the revision, and have carefully revised and proofread spelling errors and citation errors. We are sorry that we can not upload the figure during the rebuttal phase, but we really add more method details in the method d diagram to make it clear. Thank you again for your valuable suggestions.\n\n**Reference**\n\n[1] N-omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning, 2022\\\n[2] An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain, AAAI 2024\\\n[3] Fast on-device adaptation for spiking neural networks via online-within-online meta-learning, IEEE 2021\\\n[4] A two-stage spiking meta-learning method for few-shot classification[J]. Knowledge-Based Systems, 2024\\\n[5] Spatial-aware Metric Network via Patch-wise Feature Alignment for Few-shot Learning[J]. IEEE 2025.\\\n[6] Hard-Positive Prototypical Networks for Few-shot Classification, IEEE 2025.\\\n[7] EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module,2025", "claims_and_evidence": "The majority of the claims presented in the submission are backed by clear evidence, though certain aspects are problematic:\n(1) Generalization ability\nThe paper claims that the model shows strong generalization capabilities; however, the paper only evaluates it on three datasets, leaving it unclear how effectively the model would generalize to other, more diverse datasets, such as tieredimagenet, cifar, metadataset\n(2) The relevance to FSL\nThe proposed method does not address the challenges faced in few-shot learning, and its relevance to few-shot learning tasks is limited. It seems this is a general work not specified for FSL.", "methods_and_evaluation_criteria": "The benchmark is designed for few-shot learning, but the proposed method has little relevance to few-shot learning.", "theoretical_claims": "The theoretical claims about the model and losses are correct. However, the proposed method is not novel; it merely employs two simple modules on SNN. While these modules enhance performance, there is, firstly, no reasonable theoretical analysis explaining why they are effective for few-shot tasks. Secondly, these modules bear a strong resemblance to self-attention and cross-attention mechanisms, although applied within a convolutional network framework.", "experimental_designs_or_analyses": "(1) Dataset\nThe selected datasets are commonly used in few-shot learning, but with only three datasets, it is insufficient to validate the generalization capability and robustness of the proposed method.\n(2) Performance Evaluation\nAlthough comparisons are made with some SNN-FSL and SOTA few-shot learning methods, the methods being compared are outdated, rendering the comparison meaningless. There is no comparison with the latest and highest-performing methods. Even when compared, the proposed method does not surpass SOTA.\n(3) Ablation Studies\nThe ablation experiments validate the effectiveness of the two modules, but only two datasets are tested, with the third dataset remaining unverified.", "relation_to_broader_scientific_literature": "(1) Relevance to Few-Shot Learning\nThis paper aims to address the challenges faced in few-shot learning and serves as a supplement to few-shot learning research. However, the paper lacks an analysis of why the proposed method can tackle the challenges of few-shot learning, and its connection to few-shot learning is not clearly demonstrated.\n(2) Relevance to Spiking Neural Networks\nThis paper highlights that SNNs exhibit potential in few-shot learning due to their event-driven nature and low energy consumption. Existing research, such as Efficient Online Few-Shot Learning and the SOEL system, has utilized SNNs for few-shot learning, addressing some issues but still facing challenges like capturing features effectively and performing cross-class comparisons. Building on these studies, this paper proposes an SNN framework that integrates a self-feature extractor module and a cross-feature comparison module to improve feature representation and reduce energy consumption, thereby enhancing the performance of SNNs in few-shot learning. This work serves as a supplement to existing SNN-based few-shot learning research. However, the proposed method does not resolve the issues mentioned in the paper, such as lack of cross-class feature comparison and classification.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Weaknesses:\n(1) The method proposed in the paper is not novel; it simply adds two simple attention-like modules to SNN.\n(2) The proposed method does not address the challenges faced by existing SNN-FSL approaches, as mentioned in the paper, such as the struggle to effectively capture features from the complex spatiotemporal characteristics of inputs and the lack of cross-class feature comparison and classification.\n(3) The proposed method does not address the issue of data scarcity faced in few-shot learning.\n(4) The methods being compared are too outdated. It is necessary to include comparisons with the latest SOTA methods.\n(5) Since the paper mentions that the proposed method improves generalization, yet the experiments lack evidence to substantiate this claim.\n(6) The method diagrams are extremely blurry and contain significant amounts of blank space.\n(7) There are spelling errors and blank citations.(Line 018, Line 121).", "other_comments_or_suggestions": "(1) Please provide additional analyses and experiments to support your claim.\n(2) Please correct the spelling and formatting errors.", "questions_for_authors": "Please refer to the comments above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GeNcDS9LWC", "reviewer": "Reviewer_c29L", "summary": "The paper proposes a few-shot learning framework based on SNNs, which combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the insightful feedback, which has helped us clarify our contributions and identify areas for improvement. Below, we address each concern point-by-point:\n\n**Q1:Design motivation and theoretical analysis.**\n\nR1:Our paper introduces a novel framework, SSCF (Spiking Self-Cross Feature Network),  which advances SNN-based few-shot learning through two key innovations: (1) a Self-Feature Extractor that captures intra-class temporal correlations, overcoming SNNs' spatiotemporal feature extraction limitations, and (2) a Cross-Feature Contrastive module employing 4D convolutions and joint attention to optimize support-query alignment. Unlike existing SNN-FSL works focusing on spike-time encoding, our framework uniquely integrates self-correlation analysis with cross-attention mechanisms, offering more comprehensive feature learning while maintaining SNNs' energy efficiency advantages.\n\n**Q2:About the last Energy consumption model comparison.**\n\nR2:Thank you for highlighting this important aspect. Because of the real condition limitation in the lab, we are sorry that we do not have the conditions to use TPU yet. The experiments in our paper are conducted on NVIDIA 4060ti, with a performance of 22 TFLOPS. That is why we employ the energy consumption estimation based on the value 1.732mJ, which is consistent with [1]Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks,2024. Here, we further supplement the new energy consumption estimation by referring the recent energy as [2]Spiking-physformer: camera-based remote photoplethysmography with parallel spike-driven transformer,2025.\n\nSOPs= *fr* × T × FLOPs\n\nPower(ANN)=4.6pJ × FLOPs\n\nPower(SNN)=0.9pJ × SOPs\n\n| Method     | SOPs   | FLOPs  | Energy   |\n|------------|--------|--------|----------|\n| ReNet(ANN) | -      | 4.84G  | 22.264mJ |\n| SSCF(ours) | 1.39G  | 0.13G  | 1.849mJ  |\n\n**Q3:Insufficient evidence for the energy efficiency experiment, CUB dataset not achieved SOTA.**\n\nR3:Thanks for your suggestion. The significant energy reduction stems from SNNs’ event-driven, sparse processing, aligning with neuromorphic principles. While performance is slightly lower than SOTA (-3.22% in 5-way-1-shot), our method bridges SNNs and ANNs, offering a low-power alternative with minimal accuracy loss—a key contribution for resource-constrained applications.Since SNNs have great spatiotemporal dynamics thanks to the spike-based neuron computation model, thus have advantages on processing event-based neuromorphic dataset, our experiments verified that the proposed SNNs’ few shot learning method remains and even improves these advantages. On event datasets (e.g., N-Omniglot), our model achieves 84.4% (20-way-1-shot), improving +20.3% over [3], leveraging SNNs’ spatiotemporal dynamics for event-data advantages. Moreover,on *mini*ImageNet, it also outperforms [4] by +7.3%, demonstrating competitive performance.Therefore, we emphasize that our model achieves competitive performance among these datasets.\n\n\n**Q4:Comparison with methods from recent years on the N-Omniglot dataset.**\n\nR4:First of all, thank you very much for the two references you provided. [5] Knowledge Transfer for SNNs (AAAI 2024) explores static-to-event domain adaptation, which is orthogonal to our work. Their experiments are conducted on event-based datasets, and our performance on N-Omniglot surpasses it. [6] Brain-Inspired Meta-Learning (TNNLS 2024) focuses on bearing fault diagnosis, due to the data employed in this paper focus on bearing part failure problem and is not public, which makes it difficult for me to compare it under the same experimental setting. Instead, we add discussion about this study in the related work section.\n\n| Dataset    | method              | backbone | Task         | acc  |\n|------------|---------------------|----------|--------------|------|\n| **N-Omniglot** | plain           | SCNN     | 20-way 1-shot | 63.4 |\n|            | Knowledge-Transfer[2] | SCNN     |              | 64.1 |\n|            | SSCF(ours)          | SCNN     |              | 67.8 |\n|            | SSCF(ours)          | VGGSNN   |              | 84.4 |\n\nI have carefully revised and proofread the spelling errors and citation errors. Thank you again for your valuable suggestions.\n\n**Reference**\n\n[1] Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks, 2024.\\\n[2] Spiking-physformer: camera-based remote photoplethysmography with parallel spike-driven transformer. Neural Networks, 2025\\\n[3] An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain, AAAI 2024.\\\n[4] A two-stage spiking meta-learning method for few-shot classification[J]. Knowledge-Based Systems, 2024.\\\n[5] An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain, AAAI 2024.\\\n[6] Brain-Inspired Meta-Learning for Few-Shot Bearing Fault Diagnosis, TNNLS 2024", "claims_and_evidence": "Please refer to Other Strengths and Weaknesses.", "methods_and_evaluation_criteria": "It makes sense in general", "theoretical_claims": "There is a lack of theoretical analysis.", "experimental_designs_or_analyses": "Most experiments were checked. Please refer to Other Strengths and Weaknesses.", "relation_to_broader_scientific_literature": "The paper proposes a few-shot learning framework based on SNNs. In its experimental settings, the method proposed in the paper has achieved competitive results （May not be the SOTA）.", "essential_references_not_discussed": "The methods from the past two years are not limited to the following two papers. Comparing recent works with the results of this paper under the same experimental settings and assumptions is recommended.\n\n[1] Brain-Inspired Meta-Learning for Few-Shot Bearing Fault Diagnosis, TNNLS 2024\n[2] An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain,  AAAI 2023", "other_strengths_and_weaknesses": "1. The paper mainly states how to design the method, but the innovation in the SNN architecture is limited. The framework combines previous works in general, and there is a lack of design motivation and theoretical analysis.\n2. Regarding the comparison of energy efficiency results(Table 7), for the SNN, only the result of a certain FPGA from four years ago (80 GOPS/W) is presented. If the latest data of devices like the TPU are considered, generally, they can reach 4 TOPS/W. Calculated in this way, the energy consumption advantage of the SNN may not be that significant. It is recommended to conduct the comparison using the latest energy consumption models\n3. Continuing from the previous question, in the case of insufficient evidence for the energy efficiency experiment, the method proposed in the paper has not achieved state-of-the-art results under the CUB dataset.\n4. There is a lack of comparison with methods from recent years on the N-Omniglot dataset. The methods from the past two years are not limited to the following two papers. Comparing recent works with the results of this paper under the same experimental settings and assumptions is recommended\n\n[1] Brain-Inspired Meta-Learning for Few-Shot Bearing Fault Diagnosis, TNNLS 2024\n[2] An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain, AAAI 2023", "other_comments_or_suggestions": "some typos, like line 121.", "questions_for_authors": "Please refer to Other Strengths and Weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "o7gjHDvHNC", "reviewer": "Reviewer_2bcA", "summary": "This paper proposes a few-shot learning framework based on spiking neural networks (SNNs), which combines a self-feature extraction module and a cross-feature comparison module to significantly improve classification performance and reduce energy consumption. This method is innovative in using SNNs for efficient few-shot learning, and experimental results show that it outperforms existing methods on multiple data sets.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful suggestions.\n\n**Q1:The choice of the optimal λ coefficient lacks a detailed experimental basis or theoretical explanation.**\n\nR1:We appreciate this insightful feedback. We supplement the detailed experimental basis from three aspects: \n\n1.Empirical Evidence (Existing Results) Table 4 already shows λ=0.7 yields peak accuracy on CUB/*mini*ImageNet.\n\n2.Extended ablation studies will demonstrate: λ>0.5 consistently outperforms pure contrastive learning (λ=0) by +6.2% accuracy λ<0.9 avoids overfitting to temporal features (test loss ↓15%)\n\n3.Biological Plausibility:λ≈0.7 aligns with hippocampal learning studies :70% local plasticity (TET) + 30% global modulation (InfoNCE)\n\n**Q2: There are not enough visualizations in the article to better intuitively feel the effectiveness of the model.**\n\nR2: We sincerely appreciate the reviewer's valuable suggestion regarding the need for more visualizations to better demonstrate our model's effectiveness. We acknowledge that the lack of visual elements in our original submission may have reduced the paper's intuitive appeal. In response to this constructive feedback, we have enhanced our revision by incorporating spike-based visualizations that specifically illustrate the activity patterns within our model's encoder component. These visualizations clearly demonstrate how increasing time steps lead to progressive activation of features through spike emissions, effectively capturing richer representations. While we regret that we cannot share these visualization results during the rebuttal phase, we are confident they will significantly improve readers' understanding of our model's dynamic feature extraction capabilities in the final version. This addition will make the model's effectiveness more tangible and visually apparent to the research community.\n\n**Q3:.Experiments should be set up at different time steps on static datasets to further verify the effectiveness of the model.**\n\nR3:We are very grateful for your valuable suggestions. We have added experiments on static datasets CUB and *mini*ImageNet with time steps of 2, 4, and 8 to show the effect of time step T on static datasets\n| Dataset       | T | acc         |\n|---------------|---|-------------|\n| **CUB**       | 2 | 71.43±0.48  |\n|               | 4 | 73.27±0.47  |\n|               | 8 | 76.42±0.46  |\n| ***mini*ImageNet** | 2 | 60.26±0.45  |\n|               | 4 | 60.43±0.46  |\n|               | 8 | 60.75±0.57  |\n\n**Q4.:Essential References Not Discussed.**\n\nR4: In our revision, we will incorporate a dedicated discussion comparing two key approaches: (1) Zhan et al.'s two-stage spiking meta-learning method [1] that introduces a novel bio-inspired framework combining spike-timing-dependent plasticity with meta-learning for few-shot classification, and (2) Yu et al.'s graph-based few-shot learning approach [2] that proposes a unified framework bridging meta-learning, pre-training and prompting techniques for graph-structured data. While Zhan's work focuses on biologically plausible SNN architectures and Yu's emphasizes graph neural networks, our method distinguishes itself by developing an event-driven, temporally-aware architecture that achieves superior computational efficiency while maintaining competitive accuracy. These comparisons will help better position our contributions within the broader landscape of few-shot learning research.\n\n**Q5:Could the proposed model be applied to larger datasets?**\n\nR5:We sincerely appreciate this forward-looking question, which aligns perfectly with our research vision. Indeed, our architecture is designed to be extensible to larger datasets, as demonstrated by its robust performance on medium-scale benchmarks like N-Omniglot (1,623 classes) and miniImageNet (100 classes). The model's strong backbone and efficient spike-based processing make it particularly suitable for scaling up. In our ongoing and future work, we plan to systematically evaluate the model's performance on larger-scale datasets such as tieredImageNet, which will further validate its scalability and generalization capabilities while maintaining computational efficiency. This extension represents a natural and important direction for our research.\n\nI have carefully revised and proofread the spelling errors and citation errors. Thank you again for your valuable suggestions.\n\n**Reference**\n\n[1]  A two-stage spiking meta-learning method for few-shot classification[J]. Knowledge-Based Systems, 2024. \\\n[2] Few-shot learning on graphs: from meta-learning to pre-training and prompting, 2024.", "claims_and_evidence": "The high efficiency and low energy consumption of the SSCF model proposed in this paper in few-shot learning are supported by detailed experimental results, especially its excellent performance on datasets such as N-Omniglot, CUB-200-2011, and miniImageNet, showing significant performance improvement and energy consumption reduction.", "methods_and_evaluation_criteria": "The SSCF model and its evaluation criteria proposed in this paper are reasonable and effective in the few-shot learning problem. By combining the self-feature extraction module and the cross-feature comparison module, the model significantly improves the feature representation capability, which is particularly suitable for efficient learning in resource-constrained environments. In dynamic and static benchmark data sets, its superior performance in different scenarios is demonstrated in many aspects. At the same time, the training strategy combining TET Loss and InfoNCE Loss further enhances the generalization ability and robustness of the model, making this method have high practical value in dealing with a small number of sample learning problems in practical applications.", "theoretical_claims": "This paper has no theoretical proof.", "experimental_designs_or_analyses": "The experimental design and analysis in this paper are reasonable and effective as a whole. However, the selection of the optimal λ coefficient also lacks sufficient experimental basis. The existence of these problems makes the rigor of some experimental designs need to be strengthened. It is recommended to further supplement relevant analysis to improve the credibility of the experiment.", "relation_to_broader_scientific_literature": "The key contribution of this paper is to propose an SNN framework that combines self-feature extraction and cross-feature comparison, which significantly improves the classification performance and energy efficiency in few-shot learning tasks. This method innovates based on existing research, especially compared with traditional ANN methods, showing stronger generalization ability and lower energy consumption. It provides new directions and ideas for future research, especially in efficient learning in resource-constrained environments.", "essential_references_not_discussed": "The key contribution of this paper is mainly focused on the comparison with existing few-shot learning methods, but some important related works are not mentioned, such as [1][2]. \n[1] Zhan Q, Wang B, Jiang A, et al. A two-stage spiking meta-learning method for few-shot classification[J]. Knowledge-Based Systems, 2024, 284: 111220.\n[2] Yu X, Fang Y, Liu Z, et al. Few-shot learning on graphs: from meta-learning to pre-training and prompting[J]. arXiv preprint arXiv:2402.01440, 2024.", "other_strengths_and_weaknesses": "Strengths: \n1. This paper introduces a new SNN-based framework that integrates SFE and CFC modules to enhance feature representation and classification accuracy. Ablation experiments further verify the importance of these modules in improving performance. The combination of TET loss and InfoNCE loss enhances temporal dynamics and feature discrimination, making the framework more robust to noisy data. In addition, this paper has a clear structure.\n2. The method proposed in the paper shows impressive results on different datasets and is even comparable to  SOTA ANN methods.\n\nWeakness: \n1. The choice of the optimal λ coefficient lacks a detailed experimental basis or theoretical explanation.\n2. There are not enough visualizations in the article to better intuitively feel the effectiveness of the model.\n3. The experimental part lacks a comparison with the latest SNN meta-learning methods.", "other_comments_or_suggestions": "1. Experiments should be set up at different time steps on static datasets to further verify the effectiveness of the model.\n2. The proposed method should be compared with the latest SNN meta-learning methods, such as [1].\n3. There is an incorrect question mark in the first sentence of section 3.1, and the author should check the whole text carefully to avoid spelling errors.\n[1] Zhan Q, Wang B, Jiang A, et al. A two-stage spiking meta-learning method for few-shot classification[J]. Knowledge-Based Systems, 2024, 284: 111220.", "questions_for_authors": "1. Could the proposed model be applied to larger datasets?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6OoK0duBr9", "reviewer": "Reviewer_TsZa", "summary": "This paper proposes a few-shot learning framework based on a spiking neural network (SNN), combining a self-feature extraction module and a cross-feature comparison module to optimize feature representation and reduce energy consumption. The paper enhances the generalization and noise resistance of the model by combining the time-efficient training loss (TET Loss) and the InfoNCE loss. Experimental results show that the framework significantly improves the classification performance on the neuromorphic dataset N-Omniglot, and achieves performance comparable to that of an artificial neural network (ANN) on the static datasets CUB and miniImageNet, while maintaining low energy consumption. The main contribution of the paper is to propose a new SNN framework that can effectively extract spatiotemporal features in small-sample learning, and verify its superiority through experiments.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful suggestions.\n\n**Q1:The experimental part lacks the robustness analysis of the model under different datasets such as event-based neuromorphic datasets.**\n\nR1:We are very grateful for this valuable suggestion. We supplemented the performance of the model on different datasets and different noise levels to further verify the generalization performance and robustness of the model. The experimental results are shown as follows:\n\n| Dataset    | Noise | Loss    | Acc  |\n|------------|-------|---------|------|\n| **N-Omniglot** | 0.0   | CE      | 93.8 |\n|            |       | InfoNCE | 94.3 |\n|            | 0.4   | CE      | 84.2 |\n|            |       | InfoNCE | 86.5 |\n|            | 0.8   | CE      | 75.6 |\n|            |       | InfoNCE | 79.4 |\n| **CUB**       | 0.0   | CE      | 74.1 |\n|            |       | InfoNCE | 77.4 |\n|            | 0.4   | CE      | 59.8 |\n|            |       | InfoNCE | 65.7 |\n|            | 0.8   | CE      | 36.5 |\n|            |       | InfoNCE | 43.6 |\n\n**Q2:Whether the network structure of SNNs could be improved to further enhance the performance?**\n\nR2:We sincerely appreciate the reviewer's constructive suggestion. Indeed, we agree that the network architecture of spiking neural networks (SNNs) holds significant potential for further performance enhancement. While our current model has achieved state-of-the-art performance on the neuromorphic N-Omniglot dataset, we recognize several promising directions for future architectural improvements. These include exploring spiking residual connections with learnable skip weights to address the vanishing gradient problem in deep SNNs while maintaining event-driven sparsity, as well as developing sparse event-driven attention modules and spike-optimized Transformer structures that operate exclusively on active spikes. Such innovations could not only improve model performance but also further reduce energy consumption, thereby advancing the development of efficient and biologically plausible neuromorphic systems. We plan to thoroughly investigate these directions in our future work.\n\n\n**Q3.The effect of the time step T on the results on the static dataset is not shown.**\n\nR3: We are very grateful for your valuable suggestions. We have added experiments on static datasets CUB and *mini*ImageNet with time steps of 2, 4, and 8 to show the effect of time step T on static datasets.\n| Dataset       | T | acc         |\n|---------------|---|-------------|\n| **CUB**       | 2 | 71.43±0.48  |\n|               | 4 | 73.27±0.47  |\n|               | 8 | 76.42±0.46  |\n| ***mini*ImageNet** | 2 | 60.26±0.45  |\n|               | 4 | 60.43±0.46  |\n|               | 8 | 60.75±0.57  |", "claims_and_evidence": "The claims of the paper are fully supported by the experimental results. The authors conducted extensive experiments on multiple datasets to demonstrate the effectiveness of the proposed method. In particular, on the N-Omniglot dataset, the model achieved an accuracy of 98.9% in the 5-way 5-shot task, significantly outperforming existing SNN methods. In addition, the paper also provides detailed ablation experiments to verify the effectiveness of the self-feature extraction module and the cross-feature comparison module. The experimental design and result analysis are reasonable, and the data supports the main claims of the paper.", "methods_and_evaluation_criteria": "The method proposed in the paper is reasonable in few-shot learning tasks, especially for the spatiotemporal feature extraction problem of SNN. The author used multiple standard datasets (such as N-Omniglot, CUB and miniImageNet) for evaluation. These datasets are widely used in the field of small sample learning and can effectively verify the performance of the model. In addition, the paper also demonstrated the advantages of SNN in energy consumption through energy consumption calculation, further proving the practicality of the method.", "theoretical_claims": "Not applicable. This paper does not involve complex theoretical proofs.", "experimental_designs_or_analyses": "The experimental design is generally reasonable. The authors verified the performance of the model on multiple datasets and conducted ablation experiments to demonstrate the contribution of each module. However, the experimental part lacks robustness testing of the model under different kinds of datasets such as event-based neuromorphic datasets, which is an important consideration in practical applications.", "relation_to_broader_scientific_literature": "The work in this paper focuses on few-shot learning with SNNs. The innovation of this paper is to design an SNN with hybrid feature extractions modules, and further improves the performance of SNN in few-shot learning.", "essential_references_not_discussed": "This paper cites a large number of relevant literature, and it could provide more latest methods.", "other_strengths_and_weaknesses": "The main advantage of this paper is that it proposes a new SNN framework that can effectively extract spatiotemporal features in few-shot learning tasks, and verifies its superior performance and energy efficiency through experiments. However, the experimental part lacks the robustness analysis of the model under different data sets such as event-based neuromorphic datasets, which is an important consideration in practical applications.", "other_comments_or_suggestions": "1.Whether the network structure of SNNs could be improved to further enhance the performance?\n2.The experiment mentioned that the time step T has an impact on the model performance on the neuromorphic dataset N-Omniglot , but the impact of the time step T on the results on the static dataset was not shown.", "questions_for_authors": "1. Are there plans to further test the robustness of the model under different dataset noise levels?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This is a timely contribution to an important challenge in AI (few shot learning) through a biologically plausible spiking neural network. Overall, the authors provided strong responses to the reviewer's comments and the overall balance from the reviewers is that this is a valuable contribution to the field.
Decision: Accept (poster)