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..
Pixels to Graphs by Associative Embedding
Authors: Alejandro Newell, Jia Deng
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark on the Visual Genome dataset, and demonstrate state-of-the-art performance on the challenging task of scene graph generation. |
| Researcher Affiliation | Academia | Alejandro Newell Jia Deng Computer Science and Engineering University of Michigan, Ann Arbor EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository was found. |
| Open Datasets | Yes | We evaluate the performance of our method on the Visual Genome dataset [14]. Visual Genome consists of 108,077 images annotated with object detections and object-object relationships, and it serves as a challenging benchmark for scene graph generation on real world images. |
| Dataset Splits | No | The paper states, "We use the same categories, as well as the same training and test split as defined by the authors [26]", but does not provide specific percentages or counts for a validation split within the text. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU models, or memory specifications) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | We train a stacked hourglass architecture [21] in TensorFlow [1]. (While TensorFlow is mentioned, a specific version number is not provided, nor are other software dependencies with versions.) |
| Experiment Setup | Yes | The input to the network is a 512x512 image, with an output resolution of 64x64. ... doubling the number of features to 512 at the two lowest resolutions of the hourglass. The output feature length f is 256. All losses classification, bounding box regression, associative embedding are weighted equally throughout the course of training. We set so = 3 and sr = 6 which is sufficient to completely accommodate the detection annotations for all but a small fraction of cases. |