# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.5 YouCook2 Dataset: Method Performance Overview  
On the YouCook2 Dataset, various methods have been evaluated using the BLEU, METEOR, CIDEr, and ROUGE-L metrics. As shown in Table 7, several methods have reported results for this dataset, with specific models achieving notable performances. Here, we summarize the best-performing methods along with their published scores. Encoder-decoder based approaches: Encoder-decoder architectures have demonstrated strong performance on the YouCook2 Dataset. Among the listed methods, UniVL [121] stands out with a BLEU score of 17.35, METEOR score of 22.35, CIDEr score of 1.81, and ROUGE-L score of 46.52. This method effectively combines video and text representations, leveraging a unified encoder-decoder framework to generate accurate captions. Other competitive methods in this category include COTE [122] and E2 vidD6-MASSvid-BiD [21], which also report high scores across multiple metrics. Earlier works, such as VSJM-Net [22], achieved lower scores compared to more recent methods. This reflects the ongoing progress in developing advanced encoder-decoder frameworks for video description tasks. Transformer-based approaches: Transformer-based methods have shown promise on the YouCook2 Dataset, particularly in capturing the temporal dependencies and contextual information in videos. COOT [37] is a notable transformer-based approach that reports a BLEU score of 17.97, METEOR score of 19.85, CIDEr score of 57.24, and ROUGE-L score of 37.94. This method utilizes a cross-modal transformer to fuse video and text features effectively. Other transformer-based methods, such as TextKG [32] and SART [114], also exhibit competitive performance. However, it is worth noting that some transformer-based methods, like MART [39], report lower scores compared to encoder-decoder-based approaches. This suggests that the effectiveness of transformer architectures in video description tasks may be influenced by the specific design and implementation choices. \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 & COST [41] & Encoder-decoder & 11.88 & 15.70 & 29.64 & - & 2023 \\
2 & MPP-net [119] & Encoder-decoder & 12.75 & 16.01 & 29.35 & - & 2023 \\
3 & VSJM-Net [122] & Encoder-decoder & 3.97 & 12.89 & 26.52 & 25.37 & 2022 \\
4 & VC-TF [113] & Encoder-decoder & 2.76 & 7.02 & 26.55 & 18.16 & 2021 \\
5 & JSRL-VCT [96] & Encoder-decoder & 1.9 & 11.30 & 44.20 & 22.40 & 2019 \\
6 & TextKG [32] & Transformer & 11.3 & 16.5 & 26.6 & 6.3 & 2023 \\
7 & Vltim [120] & Transformer & 14.93 & 18.16 & 33.07 & 36.86 & 2023 \\
8 & SART [114] & Transformer & 11.35 & 16.21 & 28.35 & 7.18 & 2022 \\
9 & BMT-V+sm [115] & Transformer & 2.55 & 8.65 & 13.48 & 13.62 & 2021 \\
10 & COOT [37] & Transformer & 17.43 & 15.99 & 28.19 & 31.45 & 2020 \\
11 & MART [39] & Transformer & 10.85 & 15.99 & 28.19 & 6.64 & 2020 \\
12 & EC-SL [116] & Attention & 1.33 & 7.49 & 21.21 & 13.02 & 2021 \\
13 & WS-DEC [117] & Attention & 1.27 & 6.30 & 18.77 & 12.55 & 2018 \\
14 & Bi-SST [118] & Attention & 2.30 & 9.60 & 12.68 & 19.10 & 2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of video captioning methods on the ActivityNet Captions dataset, showing BLEU-4 (in percent), METEOR, CIDEr, and ROUGE-L scores per sentence for short descriptions. Attention-based approaches: Attention-based methods have been employed in video description to focus on relevant parts of the video frames and temporal sequences when generating captions. However, the performance of these methods on the YouCook2 Dataset is varied. ActBERT [123], for instance, reports a BLEU score of 5.41, METEOR score of 13.30, CIDEr score of 0.65, and ROUGE-L score of 30.56. This method incorporates both local and global attention mechanisms to capture fine-grained visual and temporal information. In contrast, Bi-SST [118] reports lower scores across all metrics.