CapGAN: A Breakthrough in Text-to-Image Synthesis

By Dr. Sampath LonkaReviewed by Susha Cheriyedath, M.Sc.Oct 11 2023

In a recent publication in the journal Information, researchers introduced CapGAN to address the challenges of generating images from textual descriptions.

Background

Text-to-image synthesis represents a challenging task within generative modeling, translating a single sentence into pixel artistry. It plays a pivotal role in fields such as computer-aided design (CAD), automatic art generation (AAG), and beyond, offering versatile applications. Yet the complexity of generating images from textual descriptions, especially for intricate scenes, remains an active area of research. In these complex scenarios, objects comprise multiple distinguishable entities, each defined by its unique color and shape.

Text-to-image synthesis holds promise in several domains: effortless scene editing, enhanced object classification, streamlined artistic creation, and data labeling. Multimodality, wherein multiple valid interpretations exist for a single input sentence, adds complexity. This means various pixel configurations can accurately represent the same text description.

Prior research has explored machine learning algorithms, particularly generative adversarial networks (GANs), but challenges persist, especially concerning spatial relationships among object entities. Capsule networks offer a breakthrough, leveraging geometric information for object recognition. Unlike convolutional neural networks (CNNs), which overlook spatial associations, capsules capture intricate spatial and orientational details.

Advancements in text-to-image synthesis

Text-to-image synthesis poses a complex multimodal challenge, necessitating shared representation across modalities and data prediction through synthesis. Although Zhu et al. harnessed AI and ML to generate images, generative modeling has revolutionized image generation from textual input. Generative modeling excels in synthesis tasks such as text-to-image synthesis, image-to-image translation, video frame prediction, super-resolution, and more. Reed et al. introduced a GAN-based architecture for text-to-pixels, employing a deep convolutional GAN, conditioned on text features from a convolutional recurrent neural network. While single-GAN text-to-image generation was successful, limitations surfaced: low-resolution and blurred images, sparse training text, and incoherent complex scene synthesis.

The text-conditioned auxiliary classifier GAN improved image resolution and object distinguishability for text-synthesized images, but mainly for single-object datasets. The model StackGAN proposed layering multiple GANs to create photorealistic images, with StackGAN++ extending this approach. However, complex-scene realism remained elusive.

Capsule networks, renowned in computer vision, have untapped potential in text-to-image synthesis. Their capacity to model hierarchical features and spatial relationships makes them promising candidates for revolutionizing this field, addressing limitations in traditional CNNs and RNNs. While some have explored capsules with GANs, their application in text-to-image synthesis awaits exploration, offering a promising avenue for innovation.

Text-to-image synthesis with CapGAN

In automatic text-to-image synthesis, the utilization of capsule networks within an adversarial framework is employed to enhance the modeling of hierarchical relationships among object entities. A straightforward yet effective model known as CapGAN is introduced to generate images based on textual input. A notable feature of CapGAN is the replacement of the final CNN layer in the discriminator with a capsule layer, which facilitates the integration of relative spatial and orientational information among diverse object entities.

The process of synthesizing photo-realistic visuals from text involves four primary phases within the proposed model: input sentences, text encoding, image generation, and image discrimination.

The initial input to the CapGAN is a single sentence from English that necessitates image synthesis. This sentence is encoded into numerical representations. This entails the utilization of skip-thought vectors, which are well-established neural network models capable of creating fixed-length representations of sentences in various natural languages. These skip-thought vectors transform text into numerical vectors suitable for generating an image through the generator of CapGAN. The generated image was fed into the discriminator for further training.

The discriminator (D) of CapGAN can process two types of inputs: real images with authentic text and synthesized (fake) images with random text. The integration of a capsule layer, alongside CNN layers, in the CapGAN discriminator enhances the retention of vital information within vectors, thus enabling the capture of relationships among distinct object entities within input images.

The CapGAN discriminator addresses three scenarios: evaluation of a real image with genuine text, assessment of a real image with counterfeit text, and judgment of a fake image with authentic text. Each scenario yields a specific output value, contributing to the computation of the overall discriminator loss. This loss function quantifies the disparity in D's outputs for real and fake images.

Evaluating CapGAN on diverse datasets

The CapGAN architecture employs a capsule network for image synthesis from textual input, addressing global coherent structures in complex scenes. Comprehensive experimentation using standardized datasets assesses the model's performance. In the experimental setup, CapGAN is evaluated on the Oxford-102 dataset for flowers, Caltech-UCSD Birds 200 for bird images, and ImageNet for dog images, utilizing ten-fold cross-validation. Evaluation metrics include the inception score (IS) and Fréchet inception distance (FID), favoring CapGAN's high IS and low FID, indicating improved image diversity and quality.

Visual results reveal CapGAN's ability to capture spatial relationships among object entities. Comparative analysis demonstrates CapGAN's superiority over prior text-to-image synthesis models, achieving the highest IS and lowest FID scores. These outcomes underscore CapGAN's efficacy in generating diverse, meaningful, and realistic images.

Conclusion

In summary, researchers introduced CapGAN, an innovative image generation model driven by adversarial training with a generator and discriminator. CapGAN's discriminator employs capsule layers, enhancing spatial relationships. Experimental results validate CapGAN's effectiveness, surpassing existing models for complex scenarios. Future work involves scaling up for higher-resolution images and exploring anti-capsule networks instead of traditional deconvolutional neural networks.