Wave-wise Discriminative Tracking by Phase-Amplitude Separation, Augmentation and Mixture
Authors: Huibin Tan, Mingyu Cao, Kun Hu, Xihuai He, Zhe Wang, Hao Li, Long Lan, Mengzhu Wang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on five benchmarks prove the effectiveness of our method. |
| Researcher Affiliation | Academia | Huibin Tan1 , Mingyu Cao1 , Kun Hu2 , Xihuai He1 , Zhe Wang3 , Hao Li1 , Long Lan1 and Mengzhu Wang4 1College of Computer Science and Technology, National University of Defense Technology 2Independent Researcher 3Hong Kong Polytechnic University 4Hebei University of Technology EMAIL, hu kun @outlook.com, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations in sections 3.1, 3.2, 3.3, and 3.4, and provides architectural diagrams in Figure 2 and Figure 3, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | The training datasets are COCO [Lin et al., 2014], La SOT [Fan et al., 2018], GOT-10k [Huang et al., 2018] and Tracking Net [M uller et al., 2018]. |
| Dataset Splits | Yes | GOT-10k is consist of 10k sequences for training and 180 videos for testing. |
| Hardware Specification | Yes | We implement our model in Python using Py Torch and train it with 8 NVIDIA A100 GPUs. And the test are conducted on a single NVIDIA RTX3070 GPU. |
| Software Dependencies | No | The paper mentions "We implement our model in Python using Py Torch" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For WDT-Vi T, we set the batch size to 24, the weight decay to 10 4, the learning rate for the backbone to 4 10 5 and the rest parameters to 4 10 4, respectively. The learning rate decreases by a factor of 10 after 240 epochs. For WDT-Hi Vi T, we set the batch size to 4, the initial learning rate of the backbone network to 2 10 5, the learning rate of other parameters to 2 10 4, and the weight decay to 10 4. The total number of training epochs is 150, and the learning rate decreases by a factor of 10 after 120 epochs. |