The paper presents a novel framework called CLIP2Depth, which aims to adapt the CLIP model for monocular dense depth estimation. The authors propose to leverage the existing prior knowledge of CLIP without direct fine-tuning, addressing the suboptimal image-text alignment by introducing a non-human language prompt embedding called "mirror." This approach demonstrates that CLIP can understand depth by predicting depths at a pixel-level, capturing depth cues while maintaining task-agnostic generalizability, and utilizing the rich semantic knowledge of the text encoder.

The framework involves fine-tuning a compact transformer module as a decoder with a frozen CLIP and jointly training a learnable embedding matrix named "mirror" to replace the subword token embeddings of CLIP. The study differentiates itself from previous literature by surpassing existing CLIP-based depth estimation models and demonstrating superior performance in spatial continuity and temporal consistency on various datasets.

The paper also discusses the concept of prompt learning and its significance in optimizing prompts for better downstream performance while preserving the model's generalizability. It highlights the shift from manual prompt engineering to automated prompt generation methods and introduces the concept of using learnable vectors as part of the input prompt to enable broader exploration of the CLIP text embedding space. The authors emphasize the potential of adjusting the suboptimal image-text correlation of pre-trained vision-language models using non-human language token embeddings, presenting a new direction for prompt learning focused on adjusting inefficient prior knowledge.

Furthermore, the paper details the key methods proposed in the study, including the mirror embedding, which aims to distill the knowledge of the frozen text encoder into a single latent embedding matrix. The authors also conduct experiments and ablation studies to validate the effectiveness of the proposed framework, demonstrating the impact of mirror initialization, pretraining approaches, and modulation methods on the performance of the CLIP2Depth model for monocular depth estimation.