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..
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation
Authors: Jiayu Xiao, Henglei Lv, Liang Li, Shuhui Wang, Qingming Huang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method outperforms existing state-of-the-art zero-shot grounded T2I generation methods by a large margin both qualitatively and quantitatively on several benchmarks. |
| Researcher Affiliation | Academia | 1Key Lab of Intelligent Information Processing, ICT, CAS, Beijing, China 2 University of Chinese Academy of Sciences, Beijing, China 3 Peng Cheng Laboratory, Shenzhen, China |
| Pseudocode | No | The paper describes the steps of the method in paragraph text and refers to figures, but does not provide a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Project page: https://sagileo.github.io/Region-and-Boundary. |
| Open Datasets | Yes | We use two benchmarks: HRS (Bakr et al., 2023) and Drawbench (Saharia et al., 2022). ... We select 100 samples from the MS-COCO (Lin et al., 2014) dataset and create triplets consisting of image caption, object phrases and bounding boxes. |
| Dataset Splits | No | The paper specifies the datasets used for evaluation but does not provide explicit training/validation/test splits, percentages, or sample counts for these datasets as defined for their experimental setup. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running its experiments. It mentions hardware in the context of a comparative method (GLIGEN) but not for their own R&B method. |
| Software Dependencies | No | The paper mentions "Stable Diffusion V-1.5" as the base model and "DDIM scheduler," but it does not list specific software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions, or other libraries). |
| Experiment Setup | Yes | We adopt the DDIM scheduler (Song et al., 2020a) with 50 denoising steps. The ratio of classifier-free guidance is set as 7.5. ... we only perform layout guidance at the first 10 steps. The λ for dynamic threshold in Eq. (6) is set as 0.4, the ratios λs and λa in Eq. (11) are set as 1.5 and 1.0 respectively. |