# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Benchmark Results
#### 5.4.7 Eight Datasets: Method Performance Overview  
Various methods have been evaluated on eight datasets using BLEU, METEOR, CIDEr, and ROUGE-L metrics, as presented in Table 9. Below, we provide an overview of the performance of these methods on each dataset. VC_NBA_2022 Dataset: On the VC_NBA_2022 dataset, KEANetT [130] stands out with the highest scores across all metrics: BLEU (32.4), METEOR (28.0), CIDEr (138.5), and ROUGE-L (54.9). This dominance is likely due to its advanced multimodal fusion and context-aware techniques for generating high-quality video descriptions. Other methods listed for this dataset are not directly comparable given KEANetT's superior performance. WTS Dataset: The WTS dataset has witnessed a variety of methods being evaluated, with significant performance differences. Among recent methods, TrafficVLM(VC) [131] achieves the highest scores: BLEU (0.419), METEOR (0.493), CIDEr (0.609), and ROUGE-L (0.589). This method's video-centric design and effective use of multimodal information likely contribute to its success. Competitive methods include CityLLaVA [132] and TrafficVLM(PC) [131], while earlier works like Video-LLaMA [133] and Video-ChatGPT [134] show lower performance, highlighting recent advancements in the field. TGIF Dataset: On the TGIF dataset, CMGNet [88] excels with the highest scores: BLEU (16.2), METEOR (18.4), CIDEr (53.7), and ROUGE-L (43.7). This method likely employs sophisticated temporal modeling and feature extraction techniques to capture video event dynamics. Other notable methods, such as SAAT [95] and RMN [80], also report competitive scores, demonstrating the effectiveness of temporal reasoning and syntactic awareness in video description tasks. TVC Dataset: The TVC dataset has seen a range of methods evaluated, with HERO [135] achieving the highest scores: BLEU (12.35), METEOR (17.64), CIDEr (49.98), and ROUGE-L (34.16). HERO's hierarchical encoding and decoding framework likely contribute to its success in capturing video content structure. Competitive methods include COTE [122] and MMT [59], suggesting that contrastive learning and multimodal fusion are particularly effective on this dataset. Charades Dataset: On the Charades dataset, HRL [25] stands out with the highest scores: BLEU (18.8), METEOR (19.5), CIDEr (23.2), and ROUGE-L (41.4). This method's hierarchical reinforcement learning approach likely generates coherent and relevant video descriptions. Given the dataset's focus on complex activities, methods that effectively model temporal dependencies and activity hierarchies tend to perform better. TACoS Dataset: The TACoS Dataset has seen evaluations from methods with varying performances. LSCEC [136] achieves notably high BLEU (47.6) but lower scores on other metrics, such as METEOR (0.352), CIDEr (1.774), and ROUGE-L (0.725). This discrepancy may suggest a focus on specific aspects of caption generation at the expense of others. JEDDi-Net [137] reports more balanced scores, indicating its effectiveness in capturing both \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** & **SODA** & **Year** \\ \hline
1 & AIEM [126] & Encoder-decoder & - & 44.9 & 11.3 & - & 11.8 & 2025 \\
2 & Streaming\_Vid2 Seq [129] & Encoder-decoder & - & 5.8 & 25.2 & - & 10.0 & 2024 \\
3 & Vid2 Seq [31] & Encoder-decoder & - & 8.5 & 43.5 & - & 13.5 & 2023 \\
4 & VidChapters-7M [125] & Encoder-decoder & - & 9.6 & 50.9 & - & 15.1 & 2023 \\
5 & SimpleConcat\_WikiHow\_TS [127] & Encoder-decoder & 1.34 & 7.97 & 0.25 & 9.21 & - & 2022 \\
6 & FAD [128] & Transformer & - & 18.9 & 37.2 & - & 10.2 & 2024 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of video captioning methods on the ViTT dataset, showing BLEU-4 (in percent), METEOR, CIDEr, ROUGE-L, and SODA scores per sentence for short descriptions. visual and textual information. These results highlight the need for holistic approaches that can perform well across all evaluation metrics.