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..
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution
Authors: Wenzhuo Liu, Fei Zhu, Shijie Ma, Cheng-lin Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in image classification, segmentation, and detection tasks demonstrate the effectiveness of MSPE, yielding superior performance on low-resolution inputs and performing comparably on high-resolution inputs with existing methods. |
| Researcher Affiliation | Academia | Wenzhuo Liu1,2, Fei Zhu3, Shijie Ma1,2, Cheng-Lin Liu1,2 1School of Artificial Intelligence, UCAS 2State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA 3Centre for Artificial Intelligence and Robotics, HKISI-CAS |
| Pseudocode | Yes | Algorithm 1 in Appendix E.2 details the training procedure of MSPE and Py Torch-style implementation. |
| Open Source Code | Yes | Code is available at https://github.com/Small Pig Peppa/ MSPE. |
| Open Datasets | Yes | We conduct experiments on 4 benchmark datasets: Image Net-1K [26] for classification tasks, ADE20K [27] and Cityscapes [28] for semantic segmentation, and COCO2017 [29] for object detection. |
| Dataset Splits | No | The paper uses benchmark datasets like Image Net-1K, ADE20K, Cityscapes, and COCO2017, which have predefined splits. However, it does not explicitly state the split percentages or sample counts for training, validation, and test sets within the paper. |
| Hardware Specification | Yes | This paper conducts experiments on a machine equipped with two AMD EPYC 7543 32-core processors; each slotted with 32 cores supporting two threads per core. The machine has 496 GB of memory and 8* NVIDIA Ge Force RTX 4090 graphics cards. |
| Software Dependencies | No | The paper mentions using open-sourced libraries such as timm, MMDetection, MMSegmentation, and PyTorch, but it does not specify the version numbers for any of these software dependencies. |
| Experiment Setup | Yes | MSPE is trained using SGD optimizer for five epochs, with a learning rate of 0.001, momentum of 0.9, weight decay of 0.0005, and batch size of 64 per GPU. |