Continuous Spatiotemporal Events Decoupling through Spike-based Bayesian Computation
Authors: Yajing Zheng, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the constructed spiking network can effectively segment the motion contained in event streams. |
| Researcher Affiliation | Academia | 1 State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University 2 Institute for Artificial Intelligence, Peking University |
| Pseudocode | Yes | Algorithm 1 Spike-based Bayesian Computation for Event Motion Segmentation |
| Open Source Code | No | We will release the code and testing data after the paper is accepted. |
| Open Datasets | Yes | Fig. 4 shows the parameter initialization process in a sequence from the Extreme Event Dataset (EED) [26], which includes both camera self-motion and a moving object. |
| Dataset Splits | No | After initializing the parameters, we select a fixed number of events in chronological order, dividing all events into different packets {en}Ng n=1 as inputs to the network over time. |
| Hardware Specification | No | The primary focus is on CPU-based verification due to minimal graphical operations, ensuring fast processing as an online learning algorithm. Additionally, the speed on both GPU and CPU is comparable. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | Parameter Initialization. The proposed spike-based Bayesian Computation framework and its corresponding event-based clustering framework are both locally convergent algorithms. ... Network Learning and Inference. After initializing the parameters, we select a fixed number of events in chronological order, dividing all events into different packets {en}Ng n=1 as inputs to the network over time. During online learning, we also split the n-th events packet {en} into different patches and feed them into the network. After optimizing the parameters θ for several epochs, we obtain the optimized motion parameters θ , and then input all events into the network to get the responsibilities P of all events belonging to different motion parameters. |