Attention over Learned Object Embeddings Enables Complex Visual Reasoning
Authors: David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt Botvinick
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {fding, felixhill, adamsantoro, mareynolds, botvinick}@google.com |
| 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. |