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..
Recognizing Ultra-High-Speed Moving Objects with Bio-Inspired Spike Camera
Authors: Junwei Zhao, Shiliang Zhang, Zhaofei Yu, Tiejun Huang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method achieves 73.2% accuracy in recognizing 10 classes of ultra-high-speed moving objects, outperforming existing spike-based recognition methods. |
| Researcher Affiliation | Academia | Junwei Zhao1,2, Shiliang Zhang1 , Zhaofei Yu1,2 , Tiejun Huang1,2 1National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University 2Institute for Artificial Intelligence, Peking University EMAIL |
| Pseudocode | No | The paper includes figures illustrating the proposed architecture and modules, but it does not contain any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Resources will be available at https://github.com/Evin-X/UHSR. |
| Open Datasets | Yes | Additionally, this paper contributes an original real-captured spiking recognition dataset consisting of 12,000 ultra-high-speed (equivalent speed > 500 km/h) moving objects. ... Resources will be available at https://github.com/Evin-X/UHSR. ... Besides above methods, this paper contributes a spiking dataset for Ultra-High-Speed object Recognition, named UHSR dataset. |
| Dataset Splits | No | Training and testing sets are spilt as a ratio of 5:1. The paper mentions training and testing splits but does not explicitly mention a separate validation set split. |
| Hardware Specification | Yes | The framework is implemented in Py Torch and trained on NVIDIA RTX 4090 GPUs. |
| Software Dependencies | No | The paper mentions "Py Torch" as the implementation framework but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | We adopt the Image Net pre-trained Res Net-18 as the backbone. Model parameters are trained using the SGD optimizer without data augmentation. The initial learning rate is set to 2e-4 with the Lambda LR scheduler. The training process runs for 30 epochs for each dataset with a batch size of 16. Input samples are downsampled to 124 124 using average pooling. θ/λ is set to 1, constraining the value of brightness intensity maps within [0, 1]. |