Explain the principles of Multi-Task Learning in Computer Vision.

Instruction: Describe what multi-task learning is and how it can be leveraged in computer vision to improve model performance across multiple tasks.

Context: This question assesses the candidate's understanding of multi-task learning strategies and their potential to enhance efficiency and accuracy in computer vision models.

Updated

Example Answer

Multi-task learning shares some model parameters across tasks, usually with separate heads. A shared backbone for segmentation and depth may reuse useful visual features, but improvement is not guaranteed. The tasks can compete, and one loss may dominate because its scale or training dynamics differ. I would compare each task with a single-task baseline, inspect gradient or loss imbalance and tune the sharing and weighting strategy on validation data.

Make it your own

Illustrative scorecard: single-task segmentation, single-task depth and shared model; report each task metric, memory and latency. A joint average must not hide a serious regression in one task.

Why this works

Sharing can reduce duplicated computation while causing negative transfer. Follow-up: when would separate later layers be preferable to a fully shared backbone?

Sources: GradNorm research.

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