MoTE: Mixture of Task Experts for Multi-Task Video Understanding

2026-08-25

AI Summary

为解决多任务视频理解中任务行为纠缠问题，提出MoTE架构，通过将大型语言模型前馈网络转换为任务特定专家来提高计算效率和准确性。

Abstract

Procedural video-language models must solve heterogeneous tasks from the same visual evidence, including action recognition, forecasting, and procedure prediction. Dense transformer decoders share the same feed-forward networks across tasks, which can entangle task behavior and make controlled capability expansion difficult. Sparse Mixture-of-Experts (MoE) decoders provide conditional computation, but token-level learned routing is not naturally aligned with task-level procedural objectives. We propose MoTE (Mixture of Task Experts), a decoder architecture that converts large language model feed-forward networks into task-specific experts while keeping the multimodal backbone shared. Each example follows one sample-level task route, so active task-expert computation remains independent of the number of stored task experts. We instantiate this design as VideoLLM-MoTE and evaluate it on five COIN benchmarks using explicit task routes. The five-expert model activates ~2B LLM parameters per sample and achieves higher average top-1 accuracy than recent VideoLLM baselines. Under the same expert topology, it improves over dense all-expert activation and learned sparse-routing controls. These results show that task-structured routing provides an interpretable and compute-efficient decoder alternative for multi-task video-language learning.

Problem

• Multi-Task Video Understanding
• Dense Transformer Decoders
• Mixture-of-Experts (MoE)
• Conditional Computation
• Task-Specific Experts

Innovation

• Mixture of Task Experts
• task-specific experts
• multimodal backbone
• task-structured routing
• multi-task video-language learning