Lightweight Prompt-Guided CLIP Adaptation for Monocular Depth Estimation
Summary: arXiv:2604.01118v1 Announce Type: cross
The application of vision-language models (VLMs) like CLIP in monocular depth estimation tasks has shown great potential. However, existing methods often require extensive fine-tuning or struggle with maintaining geometric precision. In response to these challenges, researchers have introduced a novel parameter-efficient framework called MoA-DepthCLIP, which seeks to adapt pretrained CLIP representations for monocular depth estimation with minimal supervision.
Introduction to MoA-DepthCLIP
MoA-DepthCLIP employs a lightweight Mixture-of-Adapters (MoA) module integrated into the pretrained Vision Transformer (ViT-B/32) backbone. This innovative design is complemented by selective fine-tuning of the final layers, allowing for spatially-aware adaptation. The adaptation process is guided by a global semantic context vector, which enhances the model’s ability to interpret depth information effectively.
Methodology
The framework’s architecture combines depth bin classification with direct regression through a hybrid prediction structure. This dual approach not only improves performance but also ensures that the model can leverage the rich semantic features embedded within the VLMs. In addition, a composite loss function is employed to enforce geometric constraints, which significantly enhances structural accuracy in the depth estimation process.
Performance Metrics
To evaluate the efficacy of MoA-DepthCLIP, researchers conducted experiments on the NYU Depth V2 benchmark. The results showcased the model’s competitive edge, particularly when compared to the DepthCLIP baseline. Key performance metrics include:
- δ1 Accuracy: Improved from 0.390 to 0.745
- Root Mean Square Error (RMSE): Reduced from 1.176 to 0.520
These performance improvements are notable, especially considering that MoA-DepthCLIP achieves such results with substantially fewer trainable parameters. This efficiency underscores the effectiveness of the lightweight, prompt-guided Mixture-of-Adapters in transferring knowledge from VLMs to monocular depth estimation tasks.
Conclusion
The introduction of MoA-DepthCLIP marks a significant advancement in the field of monocular depth estimation. By utilizing a lightweight architecture that requires minimal supervision, the framework not only enhances depth estimation accuracy but also paves the way for future research into efficient transfer learning techniques in computer vision. The promising results obtained on the NYU Depth V2 benchmark suggest that further exploration of this methodology could yield even more groundbreaking developments in the domain of VLMs and depth estimation.