Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Continuous Spatiotemporal Events Decoupling through Spike-based Bayesian Computation
Authors: Yajing Zheng, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang
NeurIPS 2024 | Venue PDF | 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. |