Task-Instructed Causal Routing of Vision Foundation Models for Multi-Task Learning

2026-06-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

TIGER (Task-Instruction-Guided Expert Routing) is a novel framework designed to coordinate multiple heterogeneous Vision Foundation Models (VFMs) for multi-task dense prediction. This approach addresses the inherent limitation of individual VFMs, which often struggle to capture diverse visual representations across various dense prediction tasks due to their specific pre-training objectives and data domains. TIGER leverages natural-language task instructions to guide a routing network, enabling adaptive integration of complementary expert features by assigning token-level expert weights conditioned on task semantics. Furthermore, the framework incorporates a counterfactual loss mechanism. This loss aligns routing decisions with each expert's causal contribution by measuring prediction changes when experts are excluded, fostering more reliable and interpretable routing. Evaluated on the NYUD-v2 and Pascal Context multi-task dense prediction benchmarks, TIGER consistently outperformed recent multi-task learning baselines, notably achieving these results while keeping all VFMs frozen.

Key takeaway

For Machine Learning Engineers developing multi-task vision systems, if you are encountering limitations with single Vision Foundation Models across diverse dense prediction tasks, consider adopting instruction-guided expert routing. This approach allows you to effectively coordinate heterogeneous VFMs, improving performance without retraining. Implement counterfactual causal alignment to ensure your routing decisions are reliable and interpretable, enhancing model robustness and understanding in complex multi-task scenarios.

Key insights

TIGER coordinates heterogeneous VFMs for multi-task dense prediction using instruction-guided routing and counterfactual causal alignment.

Principles

• VFMs have fragmented but complementary knowledge.
• Task instructions can guide expert integration.
• Causal contribution improves routing interpretability.

Method

TIGER employs a routing network guided by natural-language task instructions to assign token-level expert weights. It uses a counterfactual loss to align routing decisions with each expert's causal contribution by observing prediction changes upon expert exclusion.

In practice

• Integrate diverse VFMs for dense prediction.
• Use language instructions for adaptive feature routing.
• Apply counterfactual loss for interpretable expert coordination.

Topics

• Vision Foundation Models
• Multi-Task Learning
• Dense Prediction
• Expert Routing
• Causal Inference
• Natural Language Instructions

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.