Semantic-Conditional Diffusion Networks for Image Captioning (2212.03099v1)

Abstract: Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at \url{https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/scdnet}.

• The paper introduces a novel integration of diffusion models to capture word dependencies in image captioning.
• The approach uses semantic conditioning via cross-modal retrieval to align visual content with generated language.
• Experimental results on COCO show that cascaded Diffusion Transformers and self-critical training enhance caption quality.

Semantic-Conditional Diffusion Networks for Image Captioning

The presented paper explores a novel approach to image captioning, utilizing Semantic-Conditional Diffusion Networks (SCD-Net). This methodology diverges from traditional Transformer-based encoder-decoder models, leveraging diffusion models to facilitate complex visual-language alignment and coherence. The authors introduce a paradigm shift, proposing a diffusion model tailored specifically to improve image captioning tasks.

Key Contributions

1. Diffusion Model Integration: The paper highlights the integration of diffusion models into the image captioning process. Unlike traditional autoregressive models, the authors propose a non-autoregressive strategy that leverages diffusion models to capture dependencies among discrete words, promising improved text generation parallelism.
2. Semantic Conditioning: Central to the SCD-Net approach is the implementation of a semantic-conditioned diffusion process. For each image, semantically relevant sentences are retrieved using a cross-modal retrieval model. This semantic prior is used to guide a Diffusion Transformer in producing more semantically aligned and linguistically coherent captions.
3. Cascaded Diffusion Transformers: The model employs a cascade of Diffusion Transformers to progressively enhance the coherence and alignment of the generated captions. This layered structure aims to refine the output through iterative semantic reinforcement.
4. Self-Critical Sequence Training: A novel self-critical sequence training strategy is introduced to stabilize the diffusion process. This approach incorporates knowledge from an autoregressive Transformer model, enabling sequential learning with sentence-level rewards.

Experimental Results

The authors conduct extensive experiments on the COCO dataset, demonstrating the model's efficacy. Key findings include:

• The diffusion-based approach achieves performance improvements over several state-of-the-art autoregressive methods.
• SCD-Net shows strong results in metrics such as CIDEr and SPICE, indicating enhancements in semantic fidelity and linguistic quality.
• The cascaded modeling results in a progressive refinement of captions, underscoring the benefits of semantic conditioning in diffusion models.

Implications and Future Directions

The Semantic-Conditional Diffusion Networks signify a significant advancement in image captioning, capitalizing on the latent capabilities of diffusion models. This approach opens the door for further exploration into non-autoregressive models in various AI tasks, potentially extending beyond image captioning.

Future work could explore the generalizability of diffusion-based models to other domains requiring complex multimodal alignment. Additionally, enhancing the efficiency and scalability of the cascaded diffusion process presents an intriguing area for further research. The potential application of similar methodologies to tasks involving LLMs and diverse multimodal datasets remains an open and exciting avenue.

In conclusion, the paper contributes a substantial framework that not only enriches the landscape of image captioning strategies but also paves the way for broader application of conditioned diffusion processes in AI research.