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..
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 | 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. |