# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.2 MSR-VTT Dataset: Method Performance Overview  
On the MSR-VTT Dataset, numerous methods have been assessed using the BLEU, METEOR, CIDEr, and ROUGE-L metrics. As presented in Table 4, various models have \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NO** & **Method** & **Method’s Category** & **BLEU-4** & **METEOR** & **CIDEr** & **ROUGE-L** & **Year** \\ \hline
1 & ASGNet [81] & Encoder-decoder & 55.2 & 36.6 & 101.8 & 74.3 & 2025 \\
2 & CroCaps [82] & Encoder-decoder & 58.2 & 39.79 & 112.29 & 77.41 & 2025 \\  
EMSA [33] & Transformer & 60.9 & 40.5 & 117.9 & 77.9 & 2024 \\  
AM-RNN [107] & Attention & 41.3 & 32.2 & 53.9 & 68.8 & 2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of video captioning methods on the MSVD dataset, showing BLEU-4 (in percent), METEOR, CIDEr, and ROUGE-L scores per sentence for short descriptions. reported results for this dataset, with certain approaches achieving remarkable performances. Here, we provide an overview of the top-performing methods, along with their published scores. Encoder-decoder based approaches: Encoder-decoder architectures have demonstrated impressive results on the MSR-VTT dataset. Among the latest methods, GLG [87] emerges as a standout with high scores across all metrics, including BLEU (49.1), METEOR (31.9), CIDEr (63.6), and ROUGE-L (66.1). This method likely benefits from its innovative encoding and decoding strategies to produce accurate captions. Other noteworthy methods include Howtocaption [76] and ATMNet [78], both of which also report competitive scores. Earlier works, such as AVSSN [93] and RMN [80], also adopted encoder-decoder frameworks but achieved lower scores compared to the more recent methods. This underscores the significant advancements made in video description techniques over time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NO** & **Method** & **Method’s Category** & **BLEU-4** & **METEOR** & **CIDEr** & **ROUGE-L** & **Year** \\ \hline
1 & ASGNet [81] & Encoder-decoder & 43.4 & 29.6 & 52.6 & 62.5 & 2025 \\
2 & CroCaps [82] & Encoder-decoder & 44.95 & 29.28 & 53.78 & 62.86 & 2025 \\  
SRL-VCT [96] & Encoder-decoder & 42.3 & 29.7 & 49.1 & 62.8 & 2019 \\
27 & GFN-POS [27] & Encoder-decoder & 41.7 & 27.8 & 48.5 & 61.2 & 2019 \\
28 & DCM [97] & Encoder-decoder & 28.7 & 43.4 & 61.6 & 47.2 & 2019 \\
39 & SDN [110] & Encoder-decoder & 45.8 & 29.3 & 53.2 & 63.6 & 2019 \\
30 & HRL [25] & Encoder-decoder & 41.3 & 28.7 & 48.0 & 61.7 & 2018 \\
31 & CEMSA [93] & Transformer & 43.6 & 31.1 & 53.3 & 62.1 & 2024 \\
32 & SnapCap [42] & Transformer & 42.2 & 29.1 & 52.2 & 62.0 & 2024 \\
33 & TexKG [32] & Transformer & 46.6 & 30.5 & 60.8 & 64.8 & 2023 \\
34 & HMN [36] & Transformer & 43.5 & 29.0 & 51.5 & 62.7 & 2022 \\
35 & UAT-FEGs [35] & Transformer & 43.0 & 27.8 & 49.7 & 60.9 & 2022 \\
36 & O2 NA [98] & Transformer & 41.6 & 28.5 & 51.1 & 62.4 & 2021 \\
37 & SBAT [34] & Transformer & 42.9 & 28.9 & 51.6 & 61.5 & 2020 \\
38 & STGN [99] & Transformer & 40.5 & 28.3 & 47.1 & 60.9 & 2020 \\
39 & TVT [38] & Transformer & 42.46 & 28.4 & 48.53 & 61.07 & 2018 \\
40 & STAN [45] & Attention & 26.6 & 35.2 & 38.0 & 48.9 & 2025 \\
41 & IVRC [100] & Attention & 43.7 & 30.2 & 57.1 & 63.0 & 2024 \\
42 & MesNet [101] & Attention & 44.0 & 29.2 & 52.2 & 62.1 & 2023 \\
43 & ADL [47] & Attention & 26.6 & 40.2 & 44.0 & 60.2 & 2022 \\
44 & STAG [102] & Attention & 43.3 & 29.0 & 49.5 & 63.5 & 2022 \\
45 & SemSynAN [103] & Attention & 46.4 & 30.4 & 51.9 & 64.7 & 2021 \\
46 & T-DL [104] & Attention & 42.3 & 28.9 & 49.2 & 61.7 & 2021 \\
47 & SGN [105] & Attention & 40.8 & 28.3 & 49.5 & 60.8 & 2021 \\
48 & ORG-TRL [106] & Attention & 43.6 & 28.8 & 50.9 & 62.1 & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of video captioning methods on the MSR-VTT dataset, showing BLEU-4 (in percent), METEOR, CIDEr, and ROUGE-L scores per sentence for short descriptions.