The Spiking Discrete Aggregation (SDA) module is implemented, inspired by the threshold-based spike firing mechanism in Spiking Neural Networks (SNNs) and implemented using gated recurrent spiking neurons, and the Multi-Timescale Fusion (MTF) method which leverages coarse-grained temporal features from continuous event streams to further enhance the representation capability of SDA.
This work introduces Sequence-SOD, a sequence-aware SNN object detector that processes extended event sequences containing labels at multiple time points, and improves SNN object detectors' ability to exploit temporal cues while preserving the energy-efficiency benefits of sparse spiking computation.
Katharina Bendig, René Schuster, Didier Stricker· Cognitive Computation· 0 citations
A novel temporal iterative refinement (TIR) framework to obtain low-latency flow updates at high frequency with SNN-based feature encoders, and exhibits better cross-domain generalization, hinting toward the strong inductive biases of the network.
M. Humais, Hussain M. Sajwani, Sajid Javed et al.· IEEE Transactions on Image P...· 0 citations
This work establishes a new performance benchmark for classical object discovery in event data, providing a highly scalable, training-free solution for resource-constrained visual perception.
P. G. Shenwai, H. Singh, Sridhar Ravi· 0 citations
Experiments show that the proposed E-S2Feat method significantly outperforms baseline methods such as SuperEvent in pose estimation accuracy and achieves accuracy comparable to its artificial neural network counterpart while delivering an approximately 4.8-fold improvement in theoretical computational energy efficiency...
Yang Yi, Juntao Hua, Jinpu Zhang et al.· 0 citations
High-speed spike-based image sensors have shown great potential for high-speed object recognition due to their ultrahigh temporal resolution and static scene retention. To fully exploit this hardware capability, converting spike streams into structured representations suitable for deep neural networks remains a key cha...
A topology-constrained sparse convolutional network (SpTopoNet), which models the topological structure of moving object trajectories in event point clouds and introduces an event topology-aware loss function that leverages topological correlations to guide the network to maintain structural integrity of target event p...
Shiman He, Nuo Chen, Xinyi Ying et al.· 1 citation
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