Watch Before You Answer: Learning from Visually Grounded Post-Training
📰 ArXiv cs.AI
Vision-language models can answer 40-60% of video understanding questions using text cues alone, highlighting the need for visually grounded post-training
Action Steps
- Identify the limitations of current vision-language models in video understanding
- Analyze the role of text cues in answering video understanding questions
- Develop visually grounded post-training methods to improve model performance
- Evaluate the effectiveness of these methods on long video understanding benchmarks
Who Needs to Know This
AI researchers and engineers working on multimodal modeling can benefit from this study to improve video understanding performance, and product managers can use these insights to develop more effective vision-language models
Key Insight
💡 Vision-language models rely heavily on text cues, rather than visual understanding, to answer video questions
Share This
💡 Vision-language models can answer 40-60% of video questions using text cues alone! #AI #MultimodalModeling
Key Takeaways
Vision-language models can answer 40-60% of video understanding questions using text cues alone, highlighting the need for visually grounded post-training
Full Article
Title: Watch Before You Answer: Learning from Visually Grounded Post-Training
Abstract:
arXiv:2604.05117v1 Announce Type: cross Abstract: It is critical for vision-language models (VLMs) to comprehensively understand visual, temporal, and textual cues. However, despite rapid progress in multimodal modeling, video understanding performance still lags behind text-based reasoning. In this work, we find that progress is even worse than previously assumed: commonly reported long video understanding benchmarks contain 40-60% of questions that can be answered using text cues alone. Furthe
Abstract:
arXiv:2604.05117v1 Announce Type: cross Abstract: It is critical for vision-language models (VLMs) to comprehensively understand visual, temporal, and textual cues. However, despite rapid progress in multimodal modeling, video understanding performance still lags behind text-based reasoning. In this work, we find that progress is even worse than previously assumed: commonly reported long video understanding benchmarks contain 40-60% of questions that can be answered using text cues alone. Furthe
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