Spike-driven Discrete Aggregation for Event-based Object Detection

Sequence-SOD: Bio-inspired Sequence-aware Spiking Object Detection for Event Cameras

Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with high temporal resolution and a wide dynamic range. Spiking Neural Networks (SNNs) complement these sparse event streams through brain-inspired dynamics, using sparse spikes and leaky membrane potentials to integrate information over time. However, many SNN object detectors process isolated event intervals with a single label and reset the network state after each prediction, thereby underusing temporal information in continuous event streams. We introduce Sequence-SOD, a sequence-aware SNN object detector that processes extended event sequences containing labels at multiple time points. Events are accumulated into short intervals, discretized into temporal steps, and fed sequentially to an SSD-style Spiking DenseNet while preserving membrane potentials across intervals within a sequence, so that detection is driven by an evolving neural state instead of independently reset input windows. On the Gen1 Automotive Detection Dataset, sequence-aware training improves mAP from 23.38 for single-interval training to 25.30 without augmentation and to 26.88 with event-data augmentation. The model achieves a theoretical prediction frequency of 40 Hz. Training and evaluating SNN object detectors on extended event sequences improves their ability to exploit temporal cues while preserving the energy-efficiency benefits of sparse spiking computation. The results highlight sequence-aware training as a complementary direction to architectural improvements for event-based SNN detection.

Efficient High-Frequency Event-Based Optical Flow with Temporal Iterative Refinement and Spiking Neural Networks.

Existing event-based optical flow approaches often build on frame-based counterparts, failing to deliver high-frequency flow estimation. Methods that specifically address this issue fail to achieve comparable performance or the desired computational efficiency. In this work, we introduce a novel temporal iterative refinement (TIR) framework to obtain low-latency flow updates at high frequency. The TIR module incorporates the previous flow estimate along with the updated feature maps to simultaneously update and refine the flow estimate at each time step, thereby predicting accurate nonlinear pixel trajectories. However, updating the feature space at high frequency with conventional CNNs may lead to the temporal aperture problem, as the small temporal receptive field may not be enough to capture the necessary spatial context. We introduce SNN-based feature encoders to efficiently address this problem. The temporal dynamics of the SNNs provide an increased temporal receptive field, while their deployment on neuromorphic hardware offers a promising path toward additional energy efficiency. The results obtained on the real-world MVSEC dataset show that our network achieves 17× and 33× lower computations than the state-of-the-art E-RAFT and TMA, respectively, while maintaining similar accuracy performance. Compared to other supervised learning-based approaches, our network exhibits better cross-domain generalization, hinting toward the strong inductive biases of the network. To demonstrate the remarkable potential of our approach, we also provide results in extremely challenging scenarios with highly nonlinear pixel trajectories from the MultiFlow dataset, which also features high-frequency ground truth. Our code will be available at https://github.com/AhmedHumais/STIRFlow.

Real-time Unsupervised Object Discovery from Asynchronous Event Streams

Event cameras capture pixel-level intensity changes with microsecond resolution to produce highly sparse asynchronous data streams. For visual perception in latency-critical environments, we propose a lightweight, training-free framework for discovery of moving objects based on spatio-temporal clustering. This framework is driven by two core contributions. First, a linear-time Spatio-temporal Probabilistic Event Filter (SPEF) that introduces an adaptive event acceptance threshold to distinguish salient motion structures from background noise. Second, an Event Morton Code Clustering (EMCC) module that bypasses expensive distance matrix computation to efficiently group events for unsupervised discovery of moving objects. On the E-MLB dataset benchmark, SPEF achieves the best denoising performance among classical filtering methods and remains competitive with learning-based approaches without requiring any offline training. On object discovery, EMCC achieves the highest overall accuracy and lowest execution time across the FRED and eTraM datasets, outperforming established density-based clustering baselines by a substantial margin. Overall, 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. The code is available at https://github.com/PrathamShenwai/SPEF_EMCC

E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras

Benefiting from high temporal resolution and dynamic range, event-based local feature methods have attracted increasing attention. However, event sparsity, noise, and limited texture still hinder robust local feature learning. Deploying such methods on resource-constrained platforms such as unmanned aerial vehicles also requires balancing accuracy and energy efficiency. To address these challenges, this paper proposes \textbf{E-S2Feat}, a spiking neural network framework for event-based local feature detection and description. The framework jointly optimizes local feature learning from the perspectives of feature representation and selection. First, a module-specific spiking activation mechanism preserves fine-grained structural cues and discriminative information under low-bit, energy-efficient inference, thereby improving overall representation fidelity. Furthermore, a semantic-guided feature modulation mechanism leverages semantic priors to refine keypoint response distributions and enhance local descriptor discriminability, thereby guiding the model to extract local features with greater geometric stability and stronger discriminative capability. Experiments on the ECD and EDS datasets show that the proposed method significantly outperforms baseline methods such as SuperEvent in pose estimation accuracy. It also achieves accuracy comparable to its artificial neural network counterpart while delivering an approximately 4.8-fold improvement in theoretical computational energy efficiency. Visual-inertial odometry experiments on the TUM-VIE dataset further verify the effectiveness and practical application potential of the proposed method in complete SLAM systems.

Spike2Rep: Learning Compact Spatiotemporal Representations for Recognition With High-Speed Spike-Based Image Sensor

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 challenge. Notably, object categories are temporally invariant, while spike streams exhibit rich temporal dynamics, suggesting exploitable spatiotemporal redundancy. In this work, Spike2Rep is proposed to exploit the intrinsic spatiotemporal redundancy in spike streams for a compact representation. In particular, the temporal dynamics are treated as motion cues to reveal spatial importance, and temporal redundancy is used for adaptive temporal aggregation. To implement this, the discrete wavelet transform (DWT) is applied to obtain a compact time–frequency representation from spike streams. Subsequently, the temporal difference is employed to capture motion cues and generate spatial importance maps, which are added to enhance features. Furthermore, temporal and channel weights are predicted via a linear layer, which is used for adaptive modulation. Finally, the temporal averaging is adopted to produce a compact spatiotemporal representation. On the S-CALTECH dataset, the proposed method achieves up to 81.27% top-1 accuracy using 2.2 ms of spike streams and 87.73% with 6.4 ms, surpassing the prior state-of-the-art (SOTA) methods on this dataset.

Towards Persistence: Learning Topological Constraints for Event-based Small Object Detection