Images Speak in Images: A Generalist Painter for In-Context Visual Learning (2212.02499v2)

Abstract: In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images. With this idea, our training process is extremely simple, which performs standard masked image modeling on the stitch of input and output image pairs. This makes the model capable of performing tasks conditioned on visible image patches. Thus, during inference, we can adopt a pair of input and output images from the same task as the input condition, to indicate which task to perform. Without bells and whistles, our generalist Painter can achieve competitive performance compared to well-established task-specific models, on seven representative vision tasks ranging from high-level visual understanding to low-level image processing. In addition, Painter significantly outperforms recent generalist models on several challenging tasks.

• The paper introduces Painter, a novel model that converts diverse vision tasks into an image inpainting framework for unified learning.
• It employs masked image modeling with a lightweight vision Transformer, achieving competitive results across tasks like segmentation and depth estimation.
• Its unified approach simplifies task formulations and enables joint learning, paving the way for efficient generalization to new visual challenges.

Analyzing "Images Speak in Images: A Generalist Painter for In-Context Visual Learning"

The paper "Images Speak in Images: A Generalist Painter for In-Context Visual Learning" introduces Painter, a novel generalist model tailored for computer vision tasks, leveraging an "image"-centric approach to achieve in-context learning. This model redefines the paradigms of handling diverse visual tasks by transforming both the outputs and prompts of such tasks into image-based representations, thereby overcoming the significant modality differences from NLP-style in-context learning seen in LLMs.

Core Contributions

The main contribution of this research is Painter, a model that reframes vision tasks to operate within a unified image space. By representing both the outputs of computer vision tasks and the structural task prompts as images, Painter aligns the tasks' representation space, facilitating a simplified training protocol. This approach essentially proposes that many vision tasks can be reduced to an image inpainting problem, thus leveraging masked image modeling (MIM) techniques. This methodology is validated across seven representative tasks, including semantic segmentation, keypoint detection, depth estimation, and several others, achieving performance on par with specialized models without task-specific modifications to the model architecture.

Technical Approach

The core idea lies in redefining the output spaces of various tasks to align with the 3-channel image space:

• Depth Estimation: The depth values are mapped onto a [0, 255] scale similarly spread across three channels.
• Semantic Segmentation: Categories are encoded in a 3-digit $b$-base system within the RGB range.
• Keypoint Detection: Involves decoupling keypoint classification and localization, transforming them into image RGB representations.
• Instance and Panoptic Segmentation: Instance masks are colored based on the location of their centers, facilitating post-processing for segmentation tasks.

The model employs a simplified masked image modeling framework, where the architecture uses a vision Transformer encoder. The process entails training with stitched image-input/output pairs and a light three-layer head, which reconstruct outputs from visible image patches.

Performance Evaluation

Painter is compared against current state-of-the-art specialized models and other generalist systems. Across various tasks, it exhibits competitive performance with specialized models and surpasses the accuracy of some recent generalist models like Unified-IO and Pix2Seq v2. For instance, in depth estimation on NYUv2, Painter outperforms other approaches by utilizing a less complex design and showcases robust performance across semantic segmentation benchmarks too.

Theoretical and Practical Implications

The implications of this research are manifold:

• Simplicity in Task Formulations: By unifying outputs as images, the proposed model reduces the complexity in handling multiple vision tasks, providing a basis for simpler model architectures that avoid specialized head designs.
• Potential for Adapting to New Tasks: The framework holds promise for scaling to new tasks without retraining, extending the utility of visual in-context learning akin to models like GPT-3 in language.
• Enhanced Generalization: Tasks benefit mutually from joint learning within the Painter framework, potentially uncovering deeper relational structures between tasks and improving performance without task-specific tuning.

Future Directions

The research opens pathways for further exploration:

• Handling Complex Multi-modal Data: Expanding Painter's capacity to incorporate language data natively into its image-based contexts could bridge the gap between vision and language tasks more effectively.
• Scaling and Efficiency Improvements: Addressing computational efficiency, particularly in high-resolution scenarios, offers promising avenues for future work to optimize the model's applicability in real-time systems.

In conclusion, by reconsidering the fundamental representation of tasks within the visual domain, this paper advances the notion of a genuinely unified approach for handling a broad spectrum of vision tasks. Painter signifies a significant step towards versatile, general-purpose AI systems in the field of computer vision.