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 [1].
Recasting Regional Lighting for Shadow Removal
Authors: Yuhao Liu, Zhanghan Ke, Ke Xu, Fang Liu, Zhenwei Wang, Rynson W.H. Lau
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three benchmarks show that our method outperforms existing SOTA shadow removal methods. |
| Researcher Affiliation | Academia | Yuhao Liu, Zhanghan Ke, Ke Xu*, Fang Liu, Zhenwei Wang, Rynson W.H. Lau Department of Computer Science, City University of Hong Kong EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic steps but does not include formal pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state its release. |
| Open Datasets | Yes | We conduct experiments on three shadow removal datasets, i.e., SRD (Qu et al. 2017), ISTD (Wang, Li, and Yang 2018), and ISTD+ (Le and Samaras 2021). |
| Dataset Splits | No | SRD consists of 3,088 paired shadow and shadow-free images, which are split into 2680 for training and 408 for testing. ISTD contains 1,870 shadow images, shadow masks, and shadow-free image triplets, of which 1,330 are used for training and 540 for testing. (No explicit mention of validation splits for any dataset). |
| Hardware Specification | Yes | Our method is implemented via the Py Torch Toolbox on a single NVIDIA TESLA V100 GPU with 32G memory |
| Software Dependencies | No | The paper mentions 'Py Torch Toolbox' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | The initial learning rate, β1, β2, and batch size being set to 0.0002, 0.9, 0.999, and 12. Learning rate adjustment utilizes a warmup and cosine decay strategy. For the local diffusion process, we set the times steps T, initial and end variance scheduler βt to {1000,0.0001,0.02} and {50,0.0001,0.5} for training and testing. Data is augmented by random flipping and cropping, and resized to 256 256 for training. Shadow-aware decomposition and bilateral correction networks are trained for 100k and 200k iterations, respectively. |