Cross-Modal Stealth: A Coarse-to-Fine Attack Framework for RGB-T Tracker
Authors: Xinyu Xiang, Qinglong Yan, Hao Zhang, Jianfeng Ding, Han Xu, Zhongyuan Wang, Jiayi Ma
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our method. ... Experiments Experimental Settings Datasets and Evaluation Metrics We perform experiments on RGBT234 (Li et al. 2019) and Las He R (Li et al. 2022a) datasets, and evaluate our attack effectiveness using the precision rate (PR) and success rate (SR), which are the classical metrics on tracking tasks. ... Quantitative Evaluation The quantitative comparisons on the RGBT234 dataset are illustrated in Fig. 6(a)-(d)... Qualitative Evaluation As shown in Fig. 7, we visualize two groups of tracking results. ... Generalization Evaluation Moreover, we conduct generalization experiments on the Las He R dataset... Application on Physical Domain We extend our experiments into the physical domain. ... Ablation Studies We perform ablation studies to verify the validity of parameter setting and our specific designs, conducted on RGBT234 dataset against Vi PT, with results shown in Fig. 10. |
| Researcher Affiliation | Academia | 1Electronic Information School, Wuhan University, Wuhan 430072, China, 2School of Computer Science, Wuhan University, Wuhan 430072, China, 3School of Automation, Southeast University, Nanjing 210096, China EMAIL, qinglong EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using natural language and mathematical equations but does not present any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/Xinyu-Xiang/CMS |
| Open Datasets | Yes | We perform experiments on RGBT234 (Li et al. 2019) and Las He R (Li et al. 2022a) datasets |
| Dataset Splits | No | The paper mentions using RGBT234 and Las He R datasets but does not provide specific details on how these datasets were split into training, validation, or test sets, nor does it refer to a standard split by citation. |
| Hardware Specification | Yes | All experiments are conducted on the NVIDIA TITAN RTX GPU with Py Torch. |
| Software Dependencies | No | The paper mentions "Py Torch" as a software dependency but does not specify a version number. |
| Experiment Setup | Yes | For our two-stage framework, we train Stage I for 80 epochs, and then train Stage II for 30 epochs. The hyper-parameters for balancing each sub-loss are empirically set as α1 = 1.0, α2 = 0.1, α3 = 50.0, β1 = 0.1, β2 = 0.1, β3 = 50.0, and β4 = 1.0. |