# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.3 VATEX Dataset: Method Performance Overview  
On the VATEX Dataset, a variety of methods have been assessed using the BLEU, METEOR, CIDEr, and ROUGE-L metrics. As presented in Table 5, multiple approaches have reported results for this dataset, with certain models demonstrating notable performances. Here, we provide an overview of the best-performing methods along with their published scores. Encoder-decoder based approaches: Encoder-decoder architectures have exhibited promising results on the VATEX dataset. Among the listed methods, CARE [90] stands out with a BLEU score of 37.5, METEOR score of 25.1, CIDEr score of 63.1, and ROUGE-L score of 52.4. This method incorporates concept-aware reasoning to enhance video captioning. Other competitive methods include ViT/L14 [89] with notable scores across all metrics, highlighting the effectiveness of vision transformers in this task. Earlier works, such as \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 & PKG [83] & Encoder-decoder & 34.3 & 23.5 & 53.3 & 49.5 & 2025 \\
2 & KG-VCN [77] & Encoder-decoder & 33.3 & 22.9 & 53.3 & 49.5 & 2025 \\
3 & SATLF [86] & Encoder-decoder & 34.3 & 24.2 & 59.0 & 50.8 & 2024 \\
4 & MAN [28] & Encoder-decoder & 32.7 & 22.4 & 48.9 & 49.1 & 2024 \\
5 & CARE [90] & Encoder-decoder & 37.5 & 25.1 & 63.1 & 52.4 & 2023 \\
6 & VCRN [91] & Encoder-decoder & 32.4 & 22.4 & 49.9 & 48.9 & 2023 \\
7 & ViT/L14 [89] & Encoder-decoder & 35.8 & 25.3 & 64.8 & 52.0 & 2023 \\
8 & v-HRNAT [26] & Encoder-decoder & 32.1 & 21.9 & 48.5 & 48.4 & 2022 \\
9 & JRC [11] & Encoder-decoder & 33.9 & 23.7 & 57.5 & 50.2 & 2021 \\
10 & VATEX-En [112] & Transformer & 40.7 & 25.8 & 81.4 & 53.7 & 2019 \\
11 & VATEX-Ch [112] & Transformer & 32.6 & 32.1 & 59.5 & 56.5 & 2019 \\
12 & IVRC [100] & Attention & 32.8 & 24.0 & 57.4 & 50.3 & 2024 \\
13 & ORG-TRL [106] & Attention & 32.1 & 22.2 & 49.7 & 48.9 & 2020 \\
14 & FAtt(VATEX-En) [48] & Attention & 39.2 & 25.0 & 76.0 & 52.7 & 2020 \\
15 & FAtt(VATEX-Ch) [48] & Attention & 33.1 & 30.3 & 50.4 & 49.7 & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of video captioning methods on the VATEX dataset, showing BLEU-4 (in percent), METEOR, CIDEr, and ROUGE-L scores per sentence for short descriptions. vc-HRNAT [26] and RCG [111], also utilized encoder-decoder frameworks but achieved lower scores compared to more recent methods. This underscores the advancements made in video description techniques over time. Transformer-based approaches: Transformer-based methods have demonstrated their prowess in capturing long-range dependencies, making them suitable for video captioning tasks. X-Lin+Tr(VATEX-En) [112] is a prominent transformer-based approach that reports impressive scores, with a BLEU score of 40.7, METEOR score of 25.8, CIDEr score of 81.4, and ROUGE-L score of 53.7. Its counterpart, X-Lin+Tr(VATEX-Ch), also performs well, particularly in METEOR and ROUGE-L metrics, indicating its adaptability to different language settings. It is worth noting that while transformer-based methods have shown great potential, their performance can vary depending on the specific implementation and design choices, as evidenced by the difference in scores between the English and Chinese versions of X-Lin+Tr. Attention-based approaches: Attention-based methods have been extensively used in video description to focus on relevant video frames and features when generating captions.On the VATEX dataset, the performance of attention-based methods varies. FAtt(VATEX-En) [48] achieves competitive results, with a BLEU-4 score of 39.2, a METEOR score of 25.0, a CIDEr score of 76.0, and a ROUGE-L score of 52.7. This method multi-level features and attention mechanisms to improve captioning accuracy. However, other attention-based methods, such as IVRC [100] and ORG-TRL [106], report lower scores.