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..
Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner
Authors: Xing Cui, Peipei Li, Zekun Li, Xuannan Liu, Yueying Zou, Zhaofeng He
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both qualitative and quantitative comparisons demonstrate the superiority of Lucid Drag over previous methods. |
| Researcher Affiliation | Academia | 1Beijing University of Posts and Telecommunications 2University of California, Santa Barbara |
| Pseudocode | Yes | A.2 Algorithm Pipeline of Lucid Drag To facilitate the understanding of our Lucid Drag, we present the entire algorithm pipeline in Algorithm 1. Algorithm 1: Proposed Lucid Drag |
| Open Source Code | Yes | Code is available at: https://github.com/cuixing100876/Lucid Drag-Neur IPS2024. |
| Open Datasets | Yes | Following Drag Diffusion [53], we utilize the Drag Bench benchmark which is designed for the image-dragging task. |
| Dataset Splits | No | No explicit mention of validation dataset splits or usage was found, other than general training and testing. |
| Hardware Specification | Yes | The training of the discriminator can be conducted on a NVIDIA V100 GPU and the inference can be conducted on a NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | No specific software versions (e.g., PyTorch 1.9, Python 3.8) were provided for the dependencies, only general names like "Adam optimizer" and "Stable Diffusion". |
| Experiment Setup | Yes | To train the quality discriminator, we employ the Adam optimizer with a learning rate of 1e-4. We set the training epochs as 100 and the batch size as 128. For the denoising process, we adopt Stable Diffusion [51] as the base model. During sampling, the number of denoising steps is set to T = 50 with a classifier-free guidance of 5. The energy weights for gquality, gdrag and gcontent are set to 1e 3, 4e 4 and 4e 4, respectively. |