Generalized Decoding for Pixel, Image, and Language (2212.11270v1)

Abstract: We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing). Code, demo, video, and visualization are available at https://x-decoder-vl.github.io.

Generalized Decoding for Pixel, Image, and Language: A Comprehensive Analysis

This paper presents a model, X-Decoder, that synthesizes pixel-level segmentation and vision-language capabilities into a unified framework. The primary achievement of this paper is demonstrating the integration of these two traditionally separate tasks through a generalized decoder paradigm. By operating within the same semantic space, X-Decoder effectively bridges a substantial gap in the landscape of computer vision research, addressing both pixel-level and high-level language understanding tasks.

Key Contributions

1. Unified Decoding Framework: X-Decoder introduces an architecture that inherently supports both image segmentation and vision-language tasks. The model utilizes a versatile approach that can seamlessly switch between predicting pixel-level outputs for segmentation tasks and generating language tokens for tasks such as image captioning and text retrieval. This is facilitated through two types of input queries: non-semantic queries for pixel tasks and semantic queries derived from text inputs.
2. Extensive Pretraining and Evaluation: The model undergoes pretraining on a combination of a limited dataset of annotated segmentation images and millions of image-text pairs. This enables the model to possess strong transferability and adaptability across various downstream tasks. The model notably produces state-of-the-art results in open-vocabulary segmentation settings and shows competitive performance in standard benchmarks for language conditioning tasks, such as referring segmentation, even without task-specific finetuning.
3. Synergy and Flexibility: The paper illustrates how X-Decoder promotes synergy between granular visual tasks and language tasks, which are typically decoupled in standard architectures. By utilizing a shared visual-semantic space, the model benefits from mutual learning across tasks. Furthermore, the paper highlights the model's flexibility by demonstrating efficient finetuning capabilities and novel task compositions, such as referring captioning and image editing.

Implications and Future Directions

The significance of this research lies in its potential to pave the way for the development of flexible AI systems capable of understanding and generating both detailed image segmentations and associated descriptive language outputs. It challenges the conventional boundaries by showcasing an architecture that does not rely on extensive task-specific adaptations.

• Practical Benefits: This framework can significantly reduce computational resource demand and model complexity by eliminating the need for separate models for each task. The ability to process both pixel-level and language data within a unified model opens up possibilities for practical applications in image editing, detailed narrative generation, and content-based image retrieval.
• Theoretical Advancements: From a theoretical perspective, the paper advances the understanding of how cross-modal learning can occur within the same framework. Although there are existing models targeting joint vision-language tasks, X-Decoder sets itself apart by its capacity to handle fine-grained and granular vision tasks without forfeiting performance on language tasks.
• Potential for Future Research: A compelling direction for future research would be enhancing the training paradigm of X-Decoder to allow end-to-end pretraining of the entire model, including its backbone. Exploring more comprehensive supervisory signals across various granularity levels could further enhance the unified learning strategy proposed in this paper.

The paper's substantial benchmarks and rich experimental analyses underscore its robustness and applicability in advancing AI's comprehension of complex visual-linguistic tasks. The introduction of X-Decoder represents a meaningful step forward in realizing a more integrated understanding of multimodal data within the AI community.