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..
Memory Efficient Transformer Adapter for Dense Predictions
Authors: Dong Zhang, Rui Yan, Pingcheng Dong, Kwang-Ting Cheng
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, extensive evaluations on multiple representative datasets validate that META substantially enhances the predicted quality, while achieving a new state-of-the-art accuracy-efficiency trade-off. Theoretically, we demonstrate that META exhibits superior generalization capability and stronger adaptability. |
| Researcher Affiliation | Academia | Dong Zhang1,2, Rui Yan3, Pingcheng Dong1, Kwang-Ting Cheng1 1The Hong Kong University of Science and Technology 2AI Chip Center for Emerging Smart Systems (ACCESS), 3Nanjing University EMAIL;EMAIL;EMAIL |
| Pseudocode | No | The paper describes the architecture and computational processes using mathematical formulas and descriptive text, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | To facilitate a fair result comparison with existing methods, we conduct experiments, including the ablation analysis, on two commonly used datasets: MS-COCO (Caesar et al., 2018) for ODet and ISeg, and ADE20K (Zhou et al., 2017) for SSeg. |
| Dataset Splits | Yes | We report the experimental results on the val set of MS-COCO (Caesar et al., 2018), where the Image Net-1k pre-trained Vi T-B (Li et al., 2022b) is used as the backbone. For SSeg, we choose Uper Net (Xiao et al., 2018) with 160k iterations as the baseline, where the Image Net-1k pre-trained Vi T-B (Li et al., 2022b) is used as the backbone. We report the single-scale testing results on the val set of ADE20K (Zhou et al., 2017). |
| Hardware Specification | Yes | The reported inference results are measured by A100 GPUs with per-GPU batch size 2. |
| Software Dependencies | No | The paper mentions various models and baselines (e.g., Mask R-CNN, Cascade Mask R-CNN, Vi T-Adapter) but does not specify software versions for programming languages, libraries, or frameworks used for implementation (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Unless otherwise specified, these baselines are set up to be consistent with their papers and the settings of the Vi T-Adapter (Chen et al., 2022b) method. Even with different training schedules (i.e., 1 , and 3 with MS), our method can also improve the model performance, demonstrating the plug-and-play advantage of META. For SSeg, we choose Uper Net (Xiao et al., 2018) with 160k iterations as the baseline, where the Image Net-1k pre-trained Vi T-B (Li et al., 2022b) is used as the backbone. |