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..
High-Resolution Deep Image Matting
Authors: Haichao Yu, Ning Xu, Zilong Huang, Yuqian Zhou, Humphrey Shi3217-3224
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of the proposed method and its necessity for highresolution inputs. Our HDMatt approach also sets new stateof-the-art performance on Adobe Image Matting and Alpha Matting benchmarks and produce impressive visual results on more real-world high-resolution images. |
| Researcher Affiliation | Collaboration | Haichao Yu1, Ning Xu2, Zilong Huang1, Yuqian Zhou1, Humphrey Shi1,3 1UIUC, 2Adobe Research, 3University of Oregon |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is released. |
| Open Datasets | Yes | We trained our models on Adobe Image Matting (AIM) dataset (Xu et al. 2017). ... The synthetic training images will be the compositions of a foreground images in augmented AIM training set with a randomly sampled background image from COCO dataset (Lin et al. 2014). |
| Dataset Splits | No | The paper mentions training on the AIM dataset and testing on AIM and Alpha Matting benchmarks, but does not explicitly describe a validation split or specific train/validation/test partitioning methodology for reproduction. |
| Hardware Specification | No | The paper mentions 'GPU memory' as a hardware limitation, but does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Adam (Kingma and Ba 2014) optimizer was used with initial learning rate 0.5 10 3 and decayed by cosine scheduler. The model is trained for 200k steps with batch size 32 and weight decay 10 4. |