Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning | Paper Citation Record

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This paper cites write newline.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning write newline

Reference 1

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This paper cites and Rose, S.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning and Rose, S

Reference 2

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This paper cites EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

Reference 3

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This paper cites Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Reference 4

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This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Spiking deep convolutional neural networks for energy-efficient object recognition

Reference 5

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This paper cites Multi-level semantic feature augmentation for one-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Multi-level semantic feature augmentation for one-shot learning

Reference 6

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Neural networks and back propagation algorithm

Reference 7

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This paper cites Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Reference 8

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This paper cites Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training

Reference 9

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This paper cites One-shot learning of object categories.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning One-shot learning of object categories

Reference 10

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This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Model-agnostic meta-learning for fast adaptation of deep networks

Reference 11

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This paper cites Minimal solvers for relative pose estimation of multi-camera systems using affine correspondences.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Minimal solvers for relative pose estimation of multi-camera systems using affine correspondences

Reference 12

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This paper cites Cbanet: Towards complexity and bitrate adaptive deep image compression using a single network.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Cbanet: Towards complexity and bitrate adaptive deep image compression using a single network

Reference 13

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This paper cites Multidimensional pruning and its extension: A unified framework for model compression.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Multidimensional pruning and its extension: A unified framework for model compression

Reference 14

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Deep residual learning for image recognition

Reference 15

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This paper cites Momentum contrast for unsupervised visual representation learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Momentum contrast for unsupervised visual representation learning

Reference 16

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This paper cites An efficient knowledge transfer strategy for spiking neural networks from static to event domain.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning An efficient knowledge transfer strategy for spiking neural networks from static to event domain

Reference 17

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Spiking deep residual networks

Reference 18

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This paper cites Fast-snn: Fast spiking neural network by converting quantized ann.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Fast-snn: Fast spiking neural network by converting quantized ann

Reference 19

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This paper cites A bio-inspired spiking neural network with few-shot class-incremental learning for gas recognition.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning A bio-inspired spiking neural network with few-shot class-incremental learning for gas recognition

Reference 20

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Neuromorphic architectures for spiking deep neural networks

Reference 21

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This paper cites Few-shot learning in spiking neural networks by multi-timescale optimization.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Few-shot learning in spiking neural networks by multi-timescale optimization

Reference 22

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This paper cites Multi-scale metric learning for few-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Multi-scale metric learning for few-shot learning

Reference 23

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This paper cites Relational embedding for few-shot classification.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Relational embedding for few-shot classification

Reference 24

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This paper cites Siamese neural networks for one-shot image recognition.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Siamese neural networks for one-shot image recognition

Reference 25

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This paper cites G., Hann, A., and Puppe, F.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning G., Hann, A., and Puppe, F

Reference 26

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Unresolved cited work

Reference 27

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This paper cites N-omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning N-omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning

Reference 28

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This paper cites Spiking-physformer: camera-based remote photoplethysmography with parallel spike-driven transformer.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Spiking-physformer: camera-based remote photoplethysmography with parallel spike-driven transformer

Reference 29

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This paper cites Optical flow-guided 6dof object pose tracking with an event camera.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Optical flow-guided 6dof object pose tracking with an event camera

Reference 30

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Line-based 6-dof object pose estimation and tracking with an event camera

Reference 31

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Stereo event-based, 6-dof pose tracking for uncooperative spacecraft

Reference 32

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This paper cites A closer look at few-shot classification again.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning A closer look at few-shot classification again

Reference 33

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Darwin3: a large-scale neuromorphic chip with a novel isa and on-chip learning

Reference 34

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Networks of spiking neurons: the third generation of neural network models

Reference 35

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Internet of things (iot): A literature review

Reference 36

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Unresolved cited work

Reference 37

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Rapid adaptation with conditionally shifted neurons

Reference 38

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This paper cites Tadam: Task dependent adaptive metric for improved few-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Tadam: Task dependent adaptive metric for improved few-shot learning

Reference 39

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This paper cites Convolutional transformer-based few-shot learning for cross-domain hyperspectral image classification.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Convolutional transformer-based few-shot learning for cross-domain hyperspectral image classification

Reference 40

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This paper cites Transductive episodic-wise adaptive metric for few-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Transductive episodic-wise adaptive metric for few-shot learning

Reference 41

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This paper cites Deep transformer and few-shot learning for hyperspectral image classification.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Deep transformer and few-shot learning for hyperspectral image classification

Reference 42

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning and Larochelle, H

Reference 43

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This paper cites Fast on-device adaptation for spiking neural networks via online-within-online meta-learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Fast on-device adaptation for spiking neural networks via online-within-online meta-learning

Reference 44

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning One-shot learning with spiking neural networks

Reference 45

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Unresolved cited work

Reference 46

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This paper cites Efficient spiking neural networks with sparse selective activation for continual learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Efficient spiking neural networks with sparse selective activation for continual learning

Reference 47

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This paper cites Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

Reference 48

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This paper cites Prototypical networks for few-shot learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Prototypical networks for few-shot learning

Reference 49

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Optimizing the energy consumption of spiking neural networks for neuromorphic applications

Reference 50

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This paper cites B., and Neftci, E.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning B., and Neftci, E

Reference 51

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This paper cites B., and Neftci, E.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning B., and Neftci, E

Reference 52

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Unresolved cited work

Reference 53

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning H., and Hospedales, T

Reference 54

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Going deeper with convolutions

Reference 55

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Matching networks for one shot learning

Reference 56

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Few-shot learning meets transformer: Unified query-support transformers for few-shot classification

Reference 57

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Symmetric cross entropy for robust learning with noisy labels

Reference 58

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Enhancing adaptive history reserving by spiking convolutional block attention module in recurrent neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.493192Z

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Reversing structural pattern learning with biologically inspired knowledge distillation for spiking neural networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.472217Z

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning K., Pan, G., and Zhang, Q

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.456373Z

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This paper cites Heterogeneous ensemble-based spike-driven few-shot online learning.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Heterogeneous ensemble-based spike-driven few-shot online learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.441462Z

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Few-shot learning via embedding adaptation with set-to-set functions

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.426585Z

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Bayesian model-agnostic meta-learning

Reference 64

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This paper cites A two-stage spiking meta-learning method for few-shot classification.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning A two-stage spiking meta-learning method for few-shot classification

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.397874Z

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This paper cites Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers.

Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:14:23.382792Z

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Observation adb5ca9a-7645-4013-98a3-e172f11b7871 · outbound

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning Deep Meta-Learning: Learning to Learn in the Concept Space

Reference 67

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