PØDA: Prompt-driven Zero-shot Domain Adaptation (2212.03241v3)

Abstract: Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general description in natural language of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-LLM (CLIP) to optimize affine transformations of source features, steering them towards the target text embedding while preserving their content and semantics. To achieve this, we propose Prompt-driven Instance Normalization (PIN). Second, we show that these prompt-driven augmentations can be used to perform zero-shot domain adaptation for semantic segmentation. Experiments demonstrate that our method significantly outperforms CLIP-based style transfer baselines on several datasets for the downstream task at hand, even surpassing one-shot unsupervised domain adaptation. A similar boost is observed on object detection and image classification. The code is available at https://github.com/astra-vision/PODA .

Prompt-driven Zero-shot Domain Adaptation: An Expert Overview

The paper introduces a novel approach to domain adaptation, named Prompt-driven Zero-shot Domain Adaptation (PODA), which leverages the capabilities of the CLIP model to address challenges in adapting to unseen target domains using natural language prompts alone. The central idea is to adapt a model trained on a source domain to operate effectively on a target domain without access to target domain images during training. This zero-shot approach is particularly valuable in scenarios where obtaining target domain images is infeasible, making it an intriguing advancement for domain adaptation research.

Methodological Insights

1. Contrastive Vision-LLM Utilization: The paper capitalizes on the CLIP model, a recent advancement that aligns vision and language modalities via contrastive learning. By utilizing CLIP’s joint embedding space, the proposed method can effectively steer source domain features toward the target domain through textual descriptions.
2. Prompt-driven Instance Normalization (PIN): The methodology hinges on the introduction of PIN, a mechanism to compute affine transformations of source domain features. These transformations aim to approximate the style of the target domain using target text embeddings from CLIP. PIN operates by aligning the source features’ statistics according to the guidance provided by the prompt, a crucial step in achieving the desired domain adaptation effect without altering pixel-level information.
3. Zero-shot Domain Adaptation: The adaptability of PODA is validated across tasks such as semantic segmentation, object detection, and image classification. It is demonstrated through experiments that the zero-shot mechanism achieves substantial improvements over source-only and traditional style transfer approaches like CLIPstyler, reflecting the effectiveness of the prompt-driven strategy in minimizing domain gaps.

Empirical Findings

The efficacy of the approach is substantiated by quantitative results on various datasets, often surpassing those achieved by one-shot unsupervised domain adaptation methods. For instance, in the semantic segmentation task, PODA consistently outperformed baselines, both in controlled adverse conditions and synthetic-to-real domain transitions.

Theoretical and Practical Implications

Theoretically, this approach proposes a profound shift towards leveraging large-scale, multi-modal pre-trained models for domain adaptation. It suggests that textual descriptions, when harnessed in a meaningful embedding space, can significantly drive adaptation processes without explicit target domain data. Practically, this paves the way for more robust systems capable of adapting to diverse environments described in natural language, thereby reducing data acquisition burdens.

Future Directions

The exploration of prompt-driven adaptation introduces several avenues for future research. Fine-tuning the approach with varying prompt complexities, exploring ensemble methods connecting multiple prompts, and extending the methodology to other foundational models beyond CLIP are potential research opportunities. Moreover, investigating the combination of physics-based and data-driven models may enhance robustness towards domain shifts characterized by extreme variations.

In summary, this paper is a noteworthy contribution to domain adaptation literature, showcasing how the interplay between powerful vision-LLMs and creative feature manipulation can transcend traditional adaptation boundaries. This approach not only advances theoretical understanding but also holds promise for practical application in fields demanding high versatility and minimal data dependency.