Unified Text-to-Image Generation and Retrieval
(2406.05814)Abstract
How humans can efficiently and effectively acquire images has always been a perennial question. A typical solution is text-to-image retrieval from an existing database given the text query; however, the limited database typically lacks creativity. By contrast, recent breakthroughs in text-to-image generation have made it possible to produce fancy and diverse visual content, but it faces challenges in synthesizing knowledge-intensive images. In this work, we rethink the relationship between text-to-image generation and retrieval and propose a unified framework in the context of Multimodal LLMs (MLLMs). Specifically, we first explore the intrinsic discriminative abilities of MLLMs and introduce a generative retrieval method to perform retrieval in a training-free manner. Subsequently, we unify generation and retrieval in an autoregressive generation way and propose an autonomous decision module to choose the best-matched one between generated and retrieved images as the response to the text query. Additionally, we construct a benchmark called TIGeR-Bench, including creative and knowledge-intensive domains, to standardize the evaluation of unified text-to-image generation and retrieval. Extensive experimental results on TIGeR-Bench and two retrieval benchmarks, i.e., Flickr30K and MS-COCO, demonstrate the superiority and effectiveness of our proposed method.
Overview
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Qu et al. propose a novel framework that unifies text-to-image generation (T2I-G) and text-to-image retrieval (T2I-R) using Multimodal LLMs (MLLMs) to enhance visual information acquisition.
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Their model introduces bidirectional conditional likelihoods and generative retrieval with beam search, integrating a decision mechanism to adaptively choose between generation and retrieval based on semantic similarity.
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The framework's effectiveness is demonstrated through extensive experiments on the newly introduced TIGeR-Bench and standard benchmarks, outperforming existing models and highlighting the benefits of a unified approach.
Unified Text-to-Image Generation and Retrieval: An In-Depth Analysis
The paper "Unified Text-to-Image Generation and Retrieval" by Leigang Qu et al. presents a novel framework to address the complex problem of visual information acquisition by unifying text-to-image generation (T2I-G) and text-to-image retrieval (T2I-R). This comprehensive study is grounded in the development of Multimodal LLMs (MLLMs), proposing an autoregressive generative model capable of both creating novel images and retrieving existing ones. The unified approach aims to overcome the limitations associated with exclusive use of either generation or retrieval techniques.
Technical Overview
The proposed framework integrates generation and retrieval processes within one model, leveraging the intrinsic discriminative capacities of MLLMs. A key innovation lies in the use of bidirectional semantic similarity measurement, enabling efficient and effective image acquisition from diverse domains. The authors introduce several methodological advancements:
- Bidirectional Conditional Likelihoods: The discriminative ability of MLLMs is tapped into by exploiting bidirectional conditional likelihoods. This bidirectional approach uses both text-to-image and image-to-text pathways to calculate semantic similarity, enhancing the model's ability to align textual queries with appropriate visual content.
- Generative Retrieval: A novel generative retrieval process is implemented using autoregressive token decoding with beam search, effectively balancing efficiency and recall. The approach involves forward beam search for initial recall and reverse re-ranking to refine results, significantly reducing computational overhead while improving accuracy.
- Unified Decision Mechanism: The framework includes an adaptive mechanism for choosing between generated and retrieved images based on semantic similarity scores, computed using a combination of forward and reverse proxies. This decision module ensures that the system can provide the most appropriate visual response to a given textual prompt.
Benchmarking and Evaluation
A significant contribution of this paper is the introduction of TIGeR-Bench, a comprehensive benchmark dataset tailored for evaluating the unified performance of T2I-G and T2I-R systems across creative and knowledge-intensive domains. The authors conduct extensive experiments on TIGeR-Bench and two standard retrieval benchmarks (Flickr30K and MS-COCO), demonstrating the superiority of their unified approach.
Results
The experimental results highlight several key findings:
- Performance Metrics: The proposed unified model outperforms state-of-the-art MLLMs and specialized T2I-G and T2I-R models across multiple domains. For instance, the SEED-LLaMA and LaVIT variants of the unified framework achieve impressive improvements in both CLIP-T and CLIP-I scores, indicating better alignment and image quality.
- Retrieval Efficacy: On retrieval-specific tasks, the unified model consistently surpasses baseline generative and dense retrieval methods. On the MS-COCO dataset, for example, the unified model demonstrates significant gains in recall metrics, underscoring the effectiveness of the generative retrieval approach.
- Decision Mechanism: The introduction of a decision module to autonomously select between generated and retrieved images leads to notable enhancements in output relevance and quality. The adaptive decision-making process proves critical in handling diverse informational needs, particularly in complex scenarios where either generation or retrieval alone would be insufficient.
Implications and Future Directions
The theoretical and practical implications of this research are profound. By unifying T2I-G and T2I-R within a single coherent framework, the authors pave the way for more versatile and robust visual information systems. The framework's scalability and efficiency are particularly relevant for real-world applications, where the ability to flexibly generate or retrieve images based on user needs is crucial.
Practical Implications:
- Enhanced User Experience: The system's ability to choose the most relevant visual output (whether generated or retrieved) can significantly enhance user satisfaction in applications ranging from digital content creation to e-commerce and educational tools.
- Resource Efficiency: By leveraging the combined strengths of generative and retrieval models, the framework offers a resource-efficient solution that mitigates the computational demands typically associated with large-scale image databases.
Theoretical Implications:
- Advances in MLLMs: This work demonstrates the untapped potential of MLLMs in solving cross-modal tasks, encouraging further exploration into hybrid models that utilize both generative and discriminative capabilities.
- Debiasing Techniques: The study brings to light the necessity of addressing modality biases within ML models, laying a foundation for future research focused on enhancing balanced multimodal learning.
Conclusion
The paper by Qu et al. represents a significant step forward in the field of AI, offering a unified framework that elegantly blends text-to-image generation with retrieval. The method's superior performance across diverse benchmarks, combined with its efficient implementation, marks an important contribution to both theoretical research and practical applications in artificial intelligence.
Future research may explore more sophisticated decision-making algorithms, enhanced bidirectional learning techniques, and further integration of contextual understanding in multimodal frameworks, ultimately driving the development of even more dynamic and responsive AI systems.
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