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..
Image Captioning: Transforming Objects into Words
Authors: Simao Herdade, Armin Kappeler, Kofi Boakye, Joao Soares
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative results demonstrate the importance of such geometric attention for image captioning, leading to improvements on all common captioning metrics on the MS-COCO dataset. Code is available at https:// github.com/yahoo/object_relation_transformer. Our best performing model was pre-trained for 30 epochs with a softmax cross-entropy loss using the ADAM optimizer with learning rate defined as in the original Transformer paper, with 20000 warmup steps, and a batch size of 10. We trained for an additional 30 epochs using self-critical reinforcement learning [21] optimizing for CIDEr-D score, and did early-stopping for best performance on the validation set (which contains 5000 images). |
| Researcher Affiliation | Industry | Simao Herdade, Armin Kappeler, KofiBoakye, Joao Soares Yahoo Research San Francisco, CA, 94103 EMAIL, EMAIL |
| Pseudocode | No | The paper describes the algorithm using mathematical equations and text, but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Code is available at https:// github.com/yahoo/object_relation_transformer. |
| Open Datasets | Yes | We trained and evaluated our algorithm on the Microsoft COCO (MS-COCO) 2014 Captions dataset [14]. The dataset contains 113K training images with 5 human annotated captions for each image. |
| Dataset Splits | Yes | We report results on the Karpathy validation and test splits [11], which are commonly used in other image captioning publications. The dataset contains 113K training images with 5 human annotated captions for each image. The Karpathy test and validation sets contain 5K images each. |
| Hardware Specification | Yes | We ran our experiments on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions "Our algorithm was developed in Py Torch" but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | Our best performing model was pre-trained for 30 epochs with a softmax cross-entropy loss using the ADAM optimizer with learning rate defined as in the original Transformer paper, with 20000 warmup steps, and a batch size of 10. We trained for an additional 30 epochs using self-critical reinforcement learning [21] optimizing for CIDEr-D score, and did early-stopping for best performance on the validation set (which contains 5000 images). The models compared in sections 5.3-5.6 are evaluated after training for 30 epochs with standard cross-entropy loss, using ADAM optimization with the above learning rate schedule, and with batch size 15. |