Uncovering Limitations in Text-to-Image Generation: A Contrastive Approach with Structured Semantic Alignment (2023)

Abstract: Despite significant advancements in text-to-image generation models, they still face challenges when it comes to producing highly detailed or complex images based on textual descriptions. In order to explore these limitations, we propose a Structured Semantic Alignment (SSA) method for evaluating text-to-image generation models. SSA focuses on learning structured semantic embeddings across different modalities and aligning them in a joint space. The method employs the following steps to achieve its objective: (i) Generating mutated prompts by substituting words with semantically equivalent or nonequivalent alternatives while preserving the original syntax; (ii) Representing the sentence structure through parsing trees obtained via syntax parsing; (iii) Learning fine-grained structured embeddings that project semantic features from different modalities into a shared embedding space; (iv) Evaluating the semantic consistency between the structured text embeddings and the corresponding visual embeddings. Through experiments conducted on various benchmarks, we have demonstrated that SSA offers improved measurement of semantic consistency of text-to-image generation models. Additionally, it unveils a wide range of generation errors including under-generation, incorrect constituency, incorrect dependency, and semantic confusion. By uncovering these biases and limitations embedded within the models, our proposed method provides valuable insights into their shortcomings when applied to real-world scenarios.

## introduction text-to-image generation models have made significant progress over the past few years, leveraging advancements in deep learning and natural language processing techniques. these models aim to generate visually coherent and semantically relevant images from textual descriptions, pushing the boundaries of generative techniques. compared to the visual clues (segmentation maps, regional edges) adopted for single-modal image synthesis, language provides an alternative but more intuitive description. the progress in text-to-image generation models has opened up possibilities in various domains, including content creation, virtual worlds, and entertainment. the continued advancements in deep learning techniques, combined with larger and more diverse datasets, hold promise for even more realistic and context-aware image synthesis from text in the future. the journey began with early attempts using convolutional neural networks (cnns) and recurrent neural networks (rnns). however, these models struggled to capture the complex relationship between text and images. with the introduction of generative adversarial networks (gans), the field witnessed a major breakthrough. progress continued with models such as dall-e @xcite and clip @xcite leveraged reinforcement learning to generate images from textual prompts. dall-e demonstrated the ability to generate highly creative and unique images based on textual input, while clip enabled image generation conditioned on text descriptions. diffusion models have emerged as a powerful approach for image generation, capable of generating high-quality and diverse images. these models leverage the principles of diffusion processes to iteratively transform a noise vector into a realistic image. diffusion models can generate diverse and high-resolution images with rich details. they also provide a finegrained level of control over the generated images by conditioning on specific inputs. however, the effective transfer and fusion of heterogeneous information from different modalities in text-to-image generation tasks remain a significant challenge due to substantial domain gaps. moreover, there are often many-to-many mapping relationships, meaning that one image may correspond to multiple textual descriptions, and vice versa. as a result, the generated images may not always align with the text prompts provided by users, requiring manual validation and selection of satisfactory results. various aspects of the generated images, such as visual quality, object accuracy, attributes, and contextual information, need to be assessed. therefore, an automatic evaluation process and comprehensive and objective evaluation metrics are crucial for evaluating the effectiveness of text-to-image generation models. however, evaluating these models poses challenges as the generation process may not always be stable, and the perception of image quality is often subjective. currently, the primary factors considered in the evaluation process are image quality and text-image similarity. popular metrics include inception score (is) @xcite and frechet inception distance (fid) @xcite for assessing the image fidelity, and r-precision @xcite and clip score @xcite for measuring the cross-modal alignment. however, the existing metrics lack insights into the assessment of fine-grained semantic concepts, such as object attributes, context details, and semantic relationships. these factors are critical in evaluating the performance of text-to-image generation models, especially with complicated input prompts. in this context, we introduce a novel approach called structured semantic alignment (ssa) to assess the accuracy and robustness of text-to-image generation models. ssa aims to evaluate the successful transfer of joint semantics from text prompts to the generated images. the method involves generating similar prompts by replacing one word in a given sentence with a semantically similar and syntactically equivalent word. images are then generated using these prompt pairs. the underlying concept of ssa is that a reliable text-to-image generation model should consistently produce coherent and aligned results when given prompts with similar meanings. conversely, the model should generate distinct outputs when presented with prompts containing different semantic concepts. thus, when there is a mutation in the prompt, the generated image should still align with the input, indicating the model's robustness. this allows for the evaluation and diagnosis of the model's performance under varying conditions. ssa consists of the following key procedures: the implementation of the ssa methodology allows for a comprehensive evaluation of text-toimage generation models, specifically their ability to accurately transfer semantics from text prompts to the generated images. this approach serves as a valuable tool for diagnosing and enhancing the performance of such models, ensuring their reliability across diverse semantic contexts. moreover, ssa enables the identification of cases where the generated images significantly deviate from the given prompt, enabling the assessment of accuracy and robustness in text-to-image generation models. additionally, we conduct a thorough evaluation of the current state-of-the-art methods for text-to-image models, utilizing ssa to identify potential weaknesses in these models. ## challenges text-to-image generation models have made significant progress in recent years, but they still have some limitations and under-performance. the generation is heavily dependent on the quality and quantity of training data. if the training data is biased or limited, the generated images will show the contents dis-aligned with text. this problem can also be attributed to the complexity of image generation models, which rely on deep neural networks and complex optimization algorithms to generate images. these models often have a limited understanding of the semantics of the given context and may struggle to maintain semantic consistency while generating images. fine-grained semantic consistency. conditional image generation models are designed to generate images based on some given context or condition, such as a text description or a style image. however, these models often face the problem of limited ability in semantic consistency. the models can struggle to present accurate content aligned with the text description, especially when dealing with rare concepts or complex scenarios. ideally, if a model is given a text description and is tasked with generating an image based on this description, it should generate images consistent with the given context. however, due to the model's limited ability to maintain semantic consistency, it may generate an image that deviates from the given context. examples in fig. 1 show some failures in the generation of details. in the right two images, models generate "an orange car" given "an orange near a car". figure 1 : images generated by stable diffusion given "an elephant in the air", "a wooden horse stands by a river", and "an orange near a car". data biases and ambiguity. generating highquality images with fine-grained details can be challenging for text-to-image models. this can lead to generating images that are unnatural or lack important details. fine-grained details can include subtle textures, patterns, colors, and shapes that are unique to the objects or scenes described in the text. another challenge is balancing the generation of fine-grained details with overall image quality. generating high-quality images with fine-grained details requires the model to accurately represent the entities and their context from the input description, which can be a difficult task, especially when dealing with abstract concepts or complex descriptions. ## conclusion the effectiveness and robustness of text-to-image generation models are of vital importance to ensure their practicality in real-world applications. in this paper, we introduce structured semantic alignment (ssa) with contrastive learning for textto-image generation models. through our experiments, we have identified limitations of existing state-of-the-art models in text-to-image generation. our assessment highlights that these models may struggle with synthesizing semantic concepts in a given context. designing methods for overcoming the limitations and enhancing the model's ability to handle a wider range of semantic variations would be an intriguing area for future research. improving the understanding and measuring the fine-grained semantic consistency across different modalities is a critical aspect to consider in advancing multimodal generative techniques.