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..
Learning the Dynamics of Visual Relational Reasoning via Reinforced Path Routing
Authors: Chenchen Jing, Yunde Jia, Yuwei Wu, Chuanhao Li, Qi Wu1122-1130
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on referring expression comprehension and visual question answering demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | 1Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, China 2Australian Centre for Robotic Vision, University of Adelaide, Australia |
| Pseudocode | No | The paper describes the methodology and model architecture but does not include structured pseudocode or algorithm blocks that are clearly labeled or formatted as such. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We use two REC datasets: the CLEVR-Ref+ (Liu et al. 2019b) that is a synthetic diagnostic dataset, and the Ref-reasoning (Yang, Li, and Yu 2020) that contains real images. [...] The challenging GQA dataset (Hudson and Manning 2019a) that contains compositional questions about real-world images is used. |
| Dataset Splits | Yes | There are a train split and a val split in the CLEVRRef+ dataset. [...] The GQA dataset (Hudson and Manning 2019a) ... has a train split for training, a test-dev split for validation, and a test split for online testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools like the Spacy tool and various models/detectors, but it does not provide specific version numbers for these or other ancillary software components required for replication. |
| Experiment Setup | Yes | For the Ref-reasoning, the hyper-parameters ยต, ฮป and ฮณ are set as 0.01, 0.5, and 0.01. For the CLEVR-Ref+, the three hyperparameters are set as 0.01, 0.5, and 0.001. The max number of time steps is set as 4 for the Ref-reasoning and 3 for the CLEVR-Ref+. For both datasets, the dimensions of the spatial feature db and the common space d are set as 128, and 512, respectively. |