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..
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Authors: Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate that minimally contrastive examples can diagnose issues with current relation detection models as well as lead to sample-ef๏ฌcient training. |
| Researcher Affiliation | Academia | University of Michigan, Ann Arbor, MI Princeton University, Princeton, NJ |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://github.com/princeton-vl/Rel3D. |
| Open Datasets | Yes | Code and data are available at https://github.com/princeton-vl/Rel3D. |
| Dataset Splits | Yes | Hyper-parameters for each model are tuned separately using validation data, and the best-performing model on the validation set is used for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions software like Blender and Unity Web GL but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | All images are resized to 224 224 before feeding into the model. We perform random cropping and color jittering on training data. Hyper-parameters for each model are tuned separately using validation data, and the best-performing model on the validation set is used for testing. |