CLIP Can Understand Depth (2402.03251v1)

Abstract: Recent studies on generalizing CLIP for monocular depth estimation reveal that CLIP pre-trained on web-crawled data is inefficient for deriving proper similarities between image patches and depth-related prompts. In this paper, we adapt CLIP for meaningful quality of monocular depth estimation with dense prediction, without fine-tuning its original vision-language alignment. By jointly training a compact deconvolutional decoder with a tiny learnable embedding matrix named mirror, as a static prompt for its text encoder, CLIP is enabled to understand depth. With this approach, our model exhibits impressive performance matching several previous state-of-the-art vision-only models on the NYU Depth v2 and KITTI datasets, outperforming every CLIP-based depth estimation model with a large margin. Experiments on temporal depth consistency and spatial continuity demonstrate that the prior knowledge of CLIP can be effectively refined by our proposed framework. Furthermore, an ablation study on mirror proves that the resulting model estimates depth utilizing knowledge not only from the image encoder but also text encoder despite not being given any prompt written in a human way. This research demonstrates that through minimal adjustments, the prior knowledge of vision-language foundation models, such as CLIP, can be generalized even to domains where learning during pretraining is challenging. We facilitate future works focused on methods to adjust suboptimal prior knowledge of vision-LLMs using non-human language prompts, achieving performance on par with task-specific state-of-the-art methodologies.

Introduction

Contemporary research in vision-LLMing depicts Language Interpretation of Placeholders (CLIP) as an impressive multitask foundation model with broad generalization capabilities. However, its prowess staggers when faced with specialized tasks that are less represented in its training corpus, such as depth perception. This brings to light a crucial interrogation: Can CLIP truly comprehend depth to enable applications like autonomous driving that require a fine-grained understanding of spatial relationships? The researchers of the discussed paper present "CLIP2Depth," a novel framework, showcasing that CLIP, without explicit fine-tuning, can indeed be pushed to grasp the concept of depth by leveraging mirror embedings.

Methodology

"CLIP2Depth" proposes a tantalizing approach where a compact deconvolutional decoder and a set of learnable embeddings—termed "mirror"—are jointly trained. These mirror embeddings serve as a bridge, translating the language of spatial concepts to the text encoder in CLIP. Unlike previous attempts where the depth estimation process involved complex correlations between image patches and textual prompts, resulting in understanding limited depth cues, "CLIP2Depth" harnesses the power of non-human language supervision. It interprets an image as a whole, rather than piecing together fragmented information, suggesting that this method better aligns with how depth cues are processed.

Experimental Results

The empirical results are compelling. The "CLIP2Depth" framework decisively outperformed existing CLIP-based depth estimation models on both the NYU Depth v2 and KITTI benchmark datasets. Particularly notable is the model’s ability to match, and in certain metrics surpass, the performance of dedicated state-of-the-art vision-only models while conserving the task-agnostic nature of the original CLIP model. This suggests that foundation models, with minor but strategic adjustments, have a broader applicability beyond their initial training domains.

Ablation Studies and Conclusions

Further strengthening the validation of their framework, the researchers conducted methodical ablation studies. By manipulating the mirror token embeddings, they demonstrated that learning can be directed to tap into the rich semantic knowledge CLIP holds, thereby reorienting suboptimal pre-trained knowledge. Importantly, findings imply that aligned image-text correlation in highly specialized tasks can be achieved through modulation instead of calculating similarities with human-language prompts.

In conclusion, "CLIP2Depth" not only enriches the literature on the adaptability of language-vision foundation models but also forges a path for future investigations. Its success in recalibrating CLIP to understand depth without direct fine-tuning offers a lower-cost, resource-efficient method for developing robust AI systems capable of tackling complex perception tasks. This work opens new vistas for advancing the synergies between language and vision models and their deployment in real-world applications where understanding the geometry of space is crucial.