MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912225054162944 |
|---|---|
| author | Kriz, Reno Sanders, Kate Etter, David Murray, Kenton Carpenter, Cameron Van Ochten, Kelly Recknor, Hannah Guallar-Blasco, Jimena Martin, Alexander Colaianni, Ronald King, Nolan Yang, Eugene Van Durme, Benjamin |
| author_facet | Kriz, Reno Sanders, Kate Etter, David Murray, Kenton Carpenter, Cameron Van Ochten, Kelly Recknor, Hannah Guallar-Blasco, Jimena Martin, Alexander Colaianni, Ronald King, Nolan Yang, Eugene Van Durme, Benjamin |
| contents | Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce $\textbf{MultiVENT 2.0}$, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval Kriz, Reno Sanders, Kate Etter, David Murray, Kenton Carpenter, Cameron Van Ochten, Kelly Recknor, Hannah Guallar-Blasco, Jimena Martin, Alexander Colaianni, Ronald King, Nolan Yang, Eugene Van Durme, Benjamin Computer Vision and Pattern Recognition Computation and Language Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce $\textbf{MultiVENT 2.0}$, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation. |
| title | MultiVENT 2.0: A Massive Multilingual Benchmark for Event-Centric Video Retrieval |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2410.11619 |