VidMsg: A Benchmark for Implicit Message Inference in Short Videos
VidMsg benchmark evaluates implicit message understanding in short videos, enhancing retrieval with VidVec-Msg.
Key Findings
Methodology
VidMsg is constructed using a message-first data collection pipeline, employing LLMs to generate indirect search keywords and human annotations to ensure videos convey implicit messages. VidVec-Msg enhances message retrieval via synthetic storyline-message pairs, optimizing multimodal models.
Key Results
- VidVec-Msg achieves 47.5 Recall@10 in text-to-video retrieval, significantly outperforming Qwen3-VL-Emb's 39.7.
- In video-to-text retrieval, VidVec-Msg's mAP is 63.2, leading other baseline methods.
- Experiments show existing models perform poorly on implicit inference tasks, requiring multimodal cue integration.
Significance
VidMsg fills a research gap in implicit message understanding for short videos, advancing video retrieval and recommendation systems. Its multimodal reasoning capabilities enhance video content analysis accuracy, applicable in education and cultural dissemination.
Technical Contribution
VidMsg introduces a message-first data collection process and VidVec-Msg method, providing a new retrieval baseline. Unlike existing methods, it emphasizes implicit message integration, enhancing video understanding depth and breadth.
Novelty
VidMsg is the first benchmark focusing on implicit message inference in short videos, differing from traditional action recognition and video-text alignment by emphasizing multimodal information integration and reasoning.
Limitations
- Models perform poorly in distinguishing semantically similar messages, requiring further optimization.
- Dataset size is limited, potentially affecting generalization.
Future Work
Future work could expand dataset size, explore more complex multimodal fusion methods, and improve model performance in implicit message inference.
AI Executive Summary
The VidMsg benchmark aims to evaluate the understanding of implicit messages in short videos. Existing video understanding models primarily focus on visible objects and actions, overlooking the underlying messages often conveyed by video creators. VidMsg employs a message-first data collection pipeline, combined with human annotations, to construct a benchmark of 400 short videos across nine domains, including career, education, and health.
VidMsg is designed for bidirectional message-video retrieval, applicable in video search and recommendation systems. Experiments reveal that strong video-language models perform poorly on VidMsg, as the task requires pragmatic inference and contextual cue integration. The VidVec-Msg method enhances message-oriented retrieval performance through synthetic storyline-message pairs.
The introduction of VidMsg provides new directions for research in implicit message understanding in short videos. Future studies could expand the dataset size and explore more complex multimodal fusion methods to improve model performance in implicit message inference.
Deep Analysis
Background
With the rise of short videos, understanding their implicit messages has become increasingly important. Traditional video understanding methods focus on action recognition and video-text alignment, such as Kinetics and MSVD datasets. However, these methods fail to address the implicit message inference often found in short videos.
Core Problem
Implicit message inference in short videos is a complex problem involving multimodal information integration. Existing methods perform poorly in pragmatic inference and contextual cue integration, making it difficult to accurately capture the underlying messages conveyed by videos.
Innovation
VidMsg employs a message-first data collection pipeline, combined with human annotations, to ensure videos convey implicit messages without direct textual coverage. The VidVec-Msg method enhances message retrieval performance through synthetic storyline-message pairs, optimizing multimodal models.
Methodology
- �� Use LLMs to generate indirect search keywords
- �� Human annotations ensure videos convey implicit messages
- �� VidVec-Msg method enhances message retrieval performance through synthetic storyline-message pairs
Experiments
Experiments use the VidMsg dataset to evaluate existing video-language models and the VidVec-Msg method's retrieval performance. Recall@10 and mAP are used as evaluation metrics, comparing different models' performance in text-to-video and video-to-text retrieval tasks.
Results
VidVec-Msg achieves 47.5 Recall@10 in text-to-video retrieval, significantly outperforming other baseline methods. In video-to-text retrieval, VidVec-Msg's mAP is 63.2, leading other models, indicating its superiority in implicit message inference tasks.
Applications
VidMsg can enhance the accuracy of video search and recommendation systems, applicable in education and cultural dissemination. Its multimodal reasoning capabilities improve the depth and breadth of video content analysis.
Limitations & Outlook
The VidMsg dataset size is limited, potentially affecting generalization. Existing models perform poorly in distinguishing semantically similar messages, requiring further optimization. Future work could expand dataset size and explore more complex multimodal fusion methods.
Plain Language Accessible to non-experts
Imagine watching a short video that doesn't explicitly tell you what it's about, but you can feel a message like encouragement, warning, or inspiration. It's like looking at a painting; even without words, you can sense the artist's emotions or thoughts. The VidMsg benchmark evaluates whether computers can perceive these implicit messages from short videos, much like humans do.
ELI14 Explained like you're 14
Imagine watching a funny short video. It doesn't directly say it, but you feel it's encouraging you to stay positive. It's like when a teacher tells a fun story to teach you a lesson at school. VidMsg tests if computers can feel these hidden messages in videos like you can.
Glossary
Implicit Message
Information conveyed by a video that is not directly expressed.
VidMsg benchmark evaluates models' understanding of implicit messages.
Multimodal
Combining multiple information sources, such as visual, auditory, and text.
VidMsg requires models to integrate multimodal information for inference.
Retrieval
The process of finding relevant information from a large dataset.
VidMsg evaluates models' performance in message-video retrieval tasks.
LLM (Large Language Model)
A large-scale model capable of processing and generating natural language.
VidMsg uses LLMs to generate indirect search keywords.
VidVec-Msg
A method optimizing multimodal models for message retrieval.
VidVec-Msg performs well in the VidMsg benchmark.
Open Questions Unanswered questions from this research
- 1 How to improve models' ability to distinguish semantically similar messages?
- 2 How to expand dataset size to enhance model generalization?
Applications
Immediate Applications
Educational Video Recommendation
Use VidMsg to enhance the accuracy of educational video recommendations, helping students access more relevant learning resources.
Long-term Vision
Cultural Dissemination
Enhance the effectiveness of cultural video dissemination through VidMsg, promoting understanding and exchange between different cultures.
Abstract
Understanding short online videos involves more than identifying visible objects and actions; video makers often include an underlying message or purpose in the clip. We introduce VidMsg, a benchmark for evaluating implicit message understanding in short, internet-native video clips. VidMsg contains 400 YouTube-derived clips across 9 practical topic areas and 52 fine-grained target messages, covering domains such as career and finance, education, health and well-being, culture, safety, sustainability, and lifestyle. VidMsg is constructed through a message-first pipeline: an LLM first translates target messages into indirect search scenarios, which are used to retrieve candidate clips. Human annotators then retain clips that convey the intended message without being overly explicit. VidMsg is designed primarily for bidirectional message-clip retrieval for scalable applications such as video search and recommendation, where systems must capture holistic video understanding. In addition to retrieval, VidMsg includes a diagnostic multiple-choice QA benchmark, where models select the intended message of a clip from semantically related alternatives. Experiments with contemporary video-language and retrieval models show that strong models often fail on VidMsg, because the task requires pragmatic inference, integration of contextual cues, and discrimination among semantically close messages. We also introduce VidVec-Msg, a baseline method that improves message-oriented retrieval while leaving substantial headroom for future work.