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Crab$^{+}$: A Scalable and Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
arXiv – CS AI|Dongnuan Cai, Henghui Du, Chang Zhou, Xi Chen, Dan Guo, Hongyuan Zhang, Xuelong Li, Di Hu|
🤖AI Summary
Researchers developed Crab+, a new Audio-Visual Large Language Model that addresses the problem of negative transfer in multi-task learning, where 55% of tasks typically degrade when trained together. The model introduces explicit cooperation mechanisms and achieves positive transfer in 88% of tasks, outperforming both unified and specialized models.
Key Takeaways
- →Conventional multi-task audio-visual models suffer from negative transfer with 55% of tasks degrading compared to single-task training.
- →Crab+ introduces AV-UIE v2 dataset with 222K samples across 17 datasets and 7 tasks for comprehensive audio-visual understanding.
- →The model uses Interaction-aware LoRA (I-LoRA) with dynamic routing to coordinate different audio-visual interaction patterns.
- →Crab+ achieves positive transfer in nearly 88% of tasks, reversing the typical negative transfer trend in multi-task learning.
- →The unified model outperforms both existing unified models and specialized single-task models across various benchmarks.
#audio-visual#multimodal#large-language-models#machine-learning#computer-vision#multi-task-learning#ai-research#model-architecture
Read Original →via arXiv – CS AI
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