CLIP-MT: Multi-Modal Knowledge-Driven Adaptive Scale Feature Allocation for Multi-Task Dense Prediction

Recent advancements in visual multi-task learning (MTL) have sparked significant interest. However, existing dense prediction MTL methods predominantly rely on single-modality image data, limiting their performance due to the absence of complementary knowledge from other modalities. Additionally, different dense tasks exhibit heterogeneous preferences during information decoding, posing a critical challenge in effectively allocating multi-scale encoded features. To address these limitations, we propose CLIP-MT, a Multi-Modal Knowledge-Driven Adaptive Scale Feature Allocation for Multi-Task Dense Prediction. Specifically, to enrich task-shared image features with multi-modal knowledge, we introduce a novel CLIP-Guided Global Feature Enhancer (CGGF), which leverages aligned text-image information to augment object-level representations through a dual-path feature fusion architecture. Furthermore, to tackle the task-specific scale preference problem, we design an Adaptive Scale Selection Gate (ASSG), a learnable gating mechanism that dynamically selects high- or low-scale features based on task-specific demands. Finally, we integrate multi-modal and multi-scale information through a Task-Aware Feature Fusion Module (TAFF). Extensive experiments on the NYUDv2 and PASCAL-Context datasets demonstrate that CLIP-MT achieves state-of-the-art performance, outperforming existing methods across multiple dense prediction tasks.