MTV: Revisiting Multi-Task Visual Representation Learning
MTV introduces a multi-task framework that jointly optimizes a shared visual backbone using vision-language contrastive, self-supervised, and dense spatial objectives. By combining global semantics, local structure regularization, and fine-grained spatial cues derived from expert models like Depth Anything V2, the framework achieves superior performance across multiple tasks compared to single objectives.