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..
Attention over Learned Object Embeddings Enables Complex Visual Reasoning
Authors: David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested Aloe on three datasets, CLEVRER [41], CATER [12], and ACRE [44]. For each dataset, we pretrained a MONet model on individual frames. More training details and a table of hyperparameters are given in Appendix A.3; these hyperparameters were obtained through a hyperparameter sweep. All error bars are standard deviations computed over at least 5 random seeds. |
| Researcher Affiliation | Industry | David Ding Felix Hill Adam Santoro Malcolm Reynolds Matt Botvinick Deep Mind London, United Kingdom EMAIL |
| Pseudocode | Yes | In pseudo-code, global attention can be expressed as out = transformer(reshape(objects, [B, F * N, D]) and hiearchical attention as out = transformer1(reshape(objects, [B * F, N, D])) out = transformer2(reshape(out, [B, F, N * D])) . |
| Open Source Code | Yes | Model Code: https://github.com/deepmind/deepmind-research/tree/master/ object_attention_for_reasoning. |
| Open Datasets | Yes | We tested Aloe on three datasets, CLEVRER [41], CATER [12], and ACRE [44]. |
| Dataset Splits | No | The paper states 'More training details and a table of hyperparameters are given in Appendix A.3; these hyperparameters were obtained through a hyperparameter sweep.' and mentions training on 'N% of the videos and their associated labeled data', but does not explicitly provide specific train/validation/test dataset split percentages or counts in the main body. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies (e.g., library names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | More training details and a table of hyperparameters are given in Appendix A.3; these hyperparameters were obtained through a hyperparameter sweep. |