# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.6 VITT Dataset: Method Performance Overview  
On the VITT Dataset, a variety of methods have been assessed using the BLEU, METEOR, CIDEr, ROUGE-L, and SODA metrics. As presented in Table 8, multiple approaches have reported results for this dataset, with certain models demonstrating notable performances. Here, we provide an overview of the top-performing methods along with their published scores.  
Encoder-decoder based approaches: Encoder-decoder architectures have exhibited promising outcomes on the VITT dataset. Among the listed methods, VidChatpers-7M [125] stands out with a high METEOR score of 9.6, CIDEr score of 50.9, and SODA score of 15.1. This approach leverages advanced techniques to effectively generate descriptive captions. Other significant methods include Vid2 Seq [31] and AIEM [126], which also report competitive scores, particularly in the CIDEr and SODA metrics, respectively.  
Earlier works, such as SimpleConcat_WikiHow_TS [127], although not reporting scores for all metrics, show a BLEU score of 1.34 and a ROUGE-L score of 9.21. This indicates progress in video description techniques, though more recent methods have surpassed these initial efforts.  
Transformer-based approaches: Transformer-based methods have become increasingly popular due to their capacity to capture long-range dependencies. FAD [128] is a notable transformer-based approach on the VITT dataset, reporting a METEOR score of 18.9, a CIDEr score of 37.2, and a SODA score of 10.2. While this method demonstrates competitive performance, it is worth noting that other transformer-based approaches might exist that could further push the boundaries of performance on this dataset.  
However, it is evident that transformer-based methods, like FAD, may not always outperform encoder-decoder-based approaches across all metrics. This underscores the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**NO** & **Method** & **Method’s Category** & **BLEU-4** & **METEOR** & **CIDEr** & **ROUGE-L** & **Year** \\ \hline
1 & Howtocaption[76] & Encoder-decoder & 8.8 & 15.9 & 116.4 & 37.3 & 2024 \\
2 & COIT [122] & Encoder-decoder & 12.14 & 197.7 & 1.22 & 432.6 & 2024 \\
3 & COST [74] & Encoder-decoder & 8.65 & 15.62 & 1.05 & 36.5 & 2023 \\
4 & VISM-Net[21] & Encoder-decoder & 1.09 & 4.31 & 9.07 & 10.51 & 2022 \\
5 & MX-GPT [108] & Encoder-decoder & 21.26 & 26.36 & 2.14 & 48.58 & 2022 \\
6 & Explo-DMS[49]-BID [21] & Encoder-decoder & 12.04 & 18.32 & 1.23 & 39.03 & 2020 \\
7 & UniV1 [21] & Encoder-decoder & 17.35 & 22.35 & 1.81 & 46.52 & 2020 \\
8 & TextKG [32] & Transformer & 14.0 & 22.1 & 75.9 & - & 2023 \\
9 & SART [114] & Transformer & 11.43 & 19.91 & 57.66 & 8.98 & 2022 \\
10 & MAST [39] & Transformer & 11.30 & 19.85 & 57.24 & 6.69 & 2020 \\
11 & COOT [37] & Transformer & 17.97 & 19.85 & 57.24 & 37.94 & 2020 \\
12 & Videobert-SSD[124] & Transformer & 4.33 & 11.94 & 0.55 & 28.8 & 2019 \\
13 & A&RefER [123] & Attention & 5.41 & 13.30 & 0.65 & 30.56 & 2020 \\
14 & Bi-SST [118] & Attention & 2.30 & 9.60 & 12.68 & 19.10 & 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of video captioning methods on the YouCook2 dataset, showing BLEU-4 (in percent), METEOR, CIDEr, and ROUGE-L scores per sentence for short descriptions.  
importance of tailored implementations and design choices in maximizing the effectiveness of these architectures for video description tasks.