Video Object Detection with Locally-Weighted Deformable Neighbors
Authors: Zhengkai Jiang, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang, Chunhong Pan8529-8536
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU. |
| Researcher Affiliation | Collaboration | 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences 3The Chinese University of Hong Kong 4Horizon Robotics {zhengkai.jiang,chaoxu.guo,smxiang,chpan}@nlpr.ac.cn penggao@ee.cuhk.edu.hk qian01.zhang@hobot.ai |
| Pseudocode | Yes | Algorithm 1: Inference algorithm of Memory-Guided Propagation Networks |
| Open Source Code | No | No explicit statement or link providing access to open-source code for the described methodology was found. |
| Open Datasets | Yes | We evaluate the proposed method on the Image Net VID dataset which has been treated as a benchmark for video object detection (Russakovsky et al. 2015). [...] Thus we follow previous approaches and train our model on an intersection of Image Net VID and DET dataset. |
| Dataset Splits | Yes | VID dataset is split into 3862 training videos and 555 validation videos. |
| Hardware Specification | Yes | Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU. [...] For training, 4 epochs with SGD optimization method are performed on 8 GPUs with each GPU holding one mini-batch. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., libraries, frameworks) were mentioned. |
| Experiment Setup | Yes | For training, 4 epochs with SGD optimization method are performed on 8 GPUs with each GPU holding one mini-batch. Learning rate begins with 2.5e-4 and divides by 10 after 2.5 epochs. We also employ standard left-right flipping augmentation. |