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..
Visual Attention Prompted Prediction and Learning
Authors: Yifei Zhang, Bo Pan, Siyi Gu, Guangji Bai, Meikang Qiu, Xiaofeng Yang, Liang Zhao
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four datasets demonstrate the effectiveness of our proposed framework in enhancing predictions for samples both with and without prompt. |
| Researcher Affiliation | Academia | 1Emory University 2Stanford University 3Augusta University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternating Training |
| Open Source Code | Yes | Code and tools are available at https://github.com/yifeizhangcs/ visual-attention-prompt |
| Open Datasets | Yes | We employed four datasets: two from real-world scenarios, sourced from MS COCO [Lin et al., 2014], and two from the medical field, namely LIDC-IDRI (LIDC) [Armato III et al., 2011] and the Pancreas dataset [Roth et al., 2015]. |
| Dataset Splits | Yes | The final dataset included 2625 nodules and 65505 non-nodules images, split into 100/1200/1200 for training, validation, and testing to reflect limited access to human explanations. ... Data was split into 30/30/rest for training, validation, and testing, maintaining class balance. |
| Hardware Specification | Yes | Regarding computational resources, all experiments were executed using an NVIDIA GTX 3090 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The experimental setup was standardized with a batch size of 16, and the number of perturbed masks was set to 5000. Furthermore, a pixel conversion probability of 0.1 was established. The training was conducted over 10 epochs, each comprising 5 iterations for the alternating updating phase, effectively resulting in 50 training epochs for each model. The Adam optimization algorithm [Kingma and Ba, 2014] was utilized with a learning rate of 0.0001. |