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..
Bi-directional Adapter for Multimodal Tracking
Authors: Bing Cao, Junliang Guo, Pengfei Zhu, Qinghua Hu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on RGBT234 (Li et al. 2019) and Las He R (Li et al. 2021) datasets validate the effectiveness of our BAT framework. By training only a few parameters, BAT achieves significant advantages compared with the competing methods. |
| Researcher Affiliation | Academia | Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University, China EMAIL |
| Pseudocode | No | The paper describes the model architecture and method using diagrams and mathematical equations, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available: https://github.com/SparkTempest/BAT. |
| Open Datasets | Yes | We conduct experiments on two multi-modal tracking datasets: RGBT234 (Li et al. 2019) and Las He R (Li et al. 2021) |
| Dataset Splits | No | The paper mentions training on the 'Las He R training set' but does not specify details about a separate validation set or provide explicit percentages/counts for data splits (e.g., train/validation/test). |
| Hardware Specification | Yes | We implement our BAT based on the Pytorch and train it on 4 NVIDIA RTX A6000 GPUs with a batch size of 32. |
| Software Dependencies | No | The paper mentions "Pytorch" and "Adam W optimizer" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We implement our BAT based on the Pytorch and train it on 4 NVIDIA RTX A6000 GPUs with a batch size of 32. We follow the hyper-parameters setting of the foundation model in the loss function. The Adam W optimizer (Loshchilov and Hutter 2019) with a weight decay of 10^-4 is adopted, and the learning rate is set to 4 x 10^-4. The fixed parameters of the modal-specific branch in BAT are initialized by the pre-trained foundation model (Ye et al. 2022). The fine-tuning of our BAT on the Las He R training set takes 60 epochs for 8 hours, where each epoch contains 6 x 10^4 sample pairs. |