Reference Based LSTM for Image Captioning
Authors: Minghai Chen, Guiguang Ding, Sicheng Zhao, Hui Chen, Qiang Liu, Jungong Han
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method on the benchmark dataset MS COCO and the results demonstrate the significant superiority over the state-of-the-art approaches. |
| Researcher Affiliation | Academia | School of Software, Tsinghua University, Beijing 100084, China Northumbria University, Newcastle, NE1 8ST, UK |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link (1https://github.com/karpathy/neuraltalk) but it refers to a public available split of the MS COCO dataset used 'as in previous works', not to the authors' own source code for their methodology. |
| Open Datasets | Yes | we carry out experiments on the popular MS COCO dataset, which contains 123,287 images labeled with at least 5 captions by different AMT workers. Since there is no standardized split on MS COCO, we use the public available split1 as in previous works ((Karpathy and Li 2015; Xu et al. 2015; You et al. 2016), etc.). |
| Dataset Splits | Yes | Since there is no standardized split on MS COCO, we use the public available split1 as in previous works ((Karpathy and Li 2015; Xu et al. 2015; You et al. 2016), etc.). |
| Hardware Specification | No | The paper does not mention any specific hardware components such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper mentions using the VGG-16 model but does not specify any software libraries or frameworks with their version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | the beam size K used in the beam search is set to 10. ... We report the results when α1 = 0.2, α2 = 0.4 in the following experiments unless otherwise specified. |