Multi-Task Driven Feature Models for Thermal Infrared Tracking
Authors: Qiao Liu, Xin Li, Zhenyu He, Nana Fan, Di Yuan, Wei Liu, Yongsheng Liang11604-11611
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on three benchmarks show that the proposed algorithm achieves a relative gain of 10% over the baseline and performs favorably against the stateof-the-art methods. Codes and the proposed TIR dataset are available at https://github.com/Qiao Liu Hit/MMNet. |
| Researcher Affiliation | Academia | 1Harbin Institute of Technology, Shenzhen 2Shenzhen Institute of Information Technology 3Peng Cheng Laboratory |
| Pseudocode | No | The paper includes network architecture diagrams (Figure 1, Figure 2) but no pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes and the proposed TIR dataset are available at https://github.com/Qiao Liu Hit/MMNet. |
| Open Datasets | Yes | Codes and the proposed TIR dataset are available at https://github.com/Qiao Liu Hit/MMNet. We first train the proposed network on the VID2015 (Russakovsky et al. 2015) grayscale dataset with a multi-task loss: |
| Dataset Splits | No | The paper describes training on VID2015 and their constructed TIR dataset with specific epochs and learning rates, and mentions |
| Hardware Specification | Yes | We conduct the experiment using the Mat Conv Net (Vedaldi and Lenc 2015) toolbox on a PC with an i7 4.0 GHz CPU and a GTX-1080 GPU. |
| Software Dependencies | No | The paper mentions using "Mat Conv Net (Vedaldi and Lenc 2015) toolbox" but does not specify a version number for it or any other software libraries or dependencies. |
| Experiment Setup | Yes | We train the proposed network using a Stochastic Gradient Descent (SGD) method with the batch size of 8 and momentum of 0.9. At the first stage, we train the network with 60 epochs on the VID2015 dataset and the learning rate exponentially decays from 10 2 to 10 5. We set λ1 = λ2 = λ3 = 1 of Eq. 10 at all training stages. At the re-training and finetuning stages, we train the network 30 epochs with the learning rate exponentially decays from 10 3 to 10 5 on the constructed TIR dataset. In the mix-training process, we train the network 70 epochs using the same parameters with the training on VID2015 dataset. |