Visual Text Representations

• Visual text representations are methods that encode natural language into visual feature spaces, fusing linguistic and image data for retrieval, generation, and decoding.
• They employ neural architectures like Text2Vis and transformer-based visual tokenizers with shared hidden layers and attention mechanisms to achieve robust multimodal integration.
• Applications include enhanced image search, text-to-image generation, and efficient LLM input handling, demonstrating significant improvements in token reduction and computational efficiency.

Visual text representations encompass a diverse set of approaches that encode, map, or align natural language with visual features or modalities, either by translating text into the representation space of visual models or by fusing linguistic and visual information for tasks across machine perception and cognition. The field has evolved from early methods that explicitly encoded textual descriptions into visual feature spaces for retrieval, towards contemporary strategies that use visual renderings of text or integrate neural, visual, and linguistic modalities. Visual text representations today exhibit roles in cross-modal retrieval, robust language understanding, image generation, neural decoding, and efficient LLM input handling. This survey synthesizes foundational models, architectural innovations, evaluation metrics, and practical implications.

1. Neural Architectures Bridging Text and Visual Feature Spaces

Fundamental to visual text representations is the translation of language into spaces traditionally occupied by visual features extracted from neural networks. The Text2Vis architecture, for example, directly maps bag-of-words (or n-gram) text encodings into the visual embedding space of high-level convolutional layers (specifically fc6/fc7 of AlexNet trained on ImageNet), enabling image retrieval via learned cross-modal similarity (Carrara et al., 2016). A two-branch network structure is employed: a shared ReLU-activated hidden layer generates both a visual vector approximation and reconstructs the input text as an auxiliary autoencoding pathway. Optimization stochastically alternates between visual and linguistic reconstruction losses, achieving a balance between capturing precise image representations and enforcing high-level semantic regularization.

In multimodal fine-grained classification, pipelines have evolved to combine deep convolutional visual features (e.g., from GoogleNet or ResNet) with text embeddings (e.g., GloVe) extracted from recognized scene text (Bai et al., 2017, Xue et al., 2022). End-to-end networks concatenate learned visual and text feature vectors, with attention mechanisms (typically bilinear functions) used to weight and aggregate the textual components most relevant to a given visual context. This integrated approach improves both classification and retrieval accuracy in domains where scene text is semantically informative, including business place categorization and product search.

2. Approaches to Visualizing or Rendering Text for Neural Models

Departing from purely symbolic encoding, recent research has investigated the transformation of text into explicitly visual inputs. In machine translation and LLM inference settings, models such as SeeTok and the “Text-as-Image” paradigm render text as images—encoding not only character sequences but also layout, font, and overall word shape (Salesky et al., 2021, Li et al., 21 Oct 2025, Xing et al., 21 Oct 2025). The image is then partitioned into overlapping slices or patches, which are embedded by convolutional neural modules or vision encoders to yield a sequence of continuous "visual text tokens." These representations are consumed by downstream Transformer or decoder architectures.

This visual approach leads to open, continuous vocabularies—sidestepping tokenizer dependency and showing marked robustness to input noise (e.g., permutations, visually similar Unicode variants, or typographic errors) (Salesky et al., 2021, Xing et al., 21 Oct 2025). In LLM applications, input compression ratios can exceed 4.4×, with up to 70.5% reduction in floating point operations, while maintaining or surpassing performance on standard NLP tasks (Xing et al., 21 Oct 2025). Additionally, cross-lingual generalization improves as visual reading eliminates over-segmentation that plagues subword models in low-resource languages.

3. Compositional and Geometric Structure in Visual Language Spaces

Large vision-LLMs (VLMs) implicitly develop compositional latent structures in their visual embedding spaces, paralleling the compositionality observed in text representations (Berasi et al., 21 Mar 2025). The Geodesically Decomposable Embeddings (GDE) framework formalizes this phenomenon by modeling composite visual embeddings (e.g., "red chair") as exponential maps of tangent vectors—one per primitive component—originating at the intrinsic mean of normalized embeddings (the unit sphere for CLIP). This geometry-aware decomposition allows composite visual concepts to be systematically constructed from their semantic building blocks, using closed-form Riemannian exponential and logarithmic maps.

Contrary to the linear compositionality prevalent in NLP models, visual representations require respect for their manifold geometry due to contextual noise and data sparsity. GDE enables improved compositional classification, debiasing, and group robustness, outperforming linear baseline decompositions and highlighting that VLMs acquire human-like compositional reasoning in the visual domain (Berasi et al., 21 Mar 2025). The implication is a shift toward more interpretable and generalizable multimodal systems.

4. Applications in Retrieval, Generation, and Neural Decoding

Visual text representations are widely applicable:

• Cross-modal retrieval: Models that translate text queries directly into high-level visual feature spaces (as in Text2Vis) enable fast, semantics-aware image search, where textual queries find their nearest neighbors among images without reprocessing the entire database (Carrara et al., 2016).
• Text-to-image generation: The VICTR approach parses linguistic descriptions into scene graphs, encodes them with graph convolutional networks (GCNs), and aggregates object, attribute, and spatial relations into a visually contextual text embedding optimized for image synthesis tasks (Han et al., 2020). This fosters richer and more semantically grounded generation, as evidenced by improved Inception Scores and lower FIDs when used as a text encoder in StackGAN, AttnGAN, and DM-GAN.
• Visual writing and story editing: Dedicated systems for “visual writing” integrate timeline, entity, location, and event diagrams with text editors, supporting creative exploration and precise, consistent narrative revision by manipulating visual structures instead of raw language (Masson et al., 9 Oct 2024).
• EEG-decoding and neural data alignment: Multimodal frameworks such as HMAVD use shared embedding spaces to jointly align EEG signals, image content, and textual semantics, leveraging adapter modules and dynamic balancing strategies (e.g., MCDB and SPR) to harmonize modalities of different strengths and distributions (Sun et al., 3 Sep 2025). Explicit text features act as scaffolding, reducing the negative impact of noise and instability in neural decoding.

5. Efficiency, Token Compression, and Robustness

Visual text representations offer distinct advantages in model efficiency and input handling. By rendering long documents as images and processing them with vision encoders, the total token count seen by multimodal decoder LLMs is nearly halved, without compromising accuracy in retrieval or summarization tasks (Li et al., 21 Oct 2025). SeeTok achieves up to 4.43× token reduction and 70.5% less computation, while outperforming or matching subword tokenizers on language understanding tasks, enhancing generalization, and providing greater robustness to typographic noise or script variation (Xing et al., 21 Oct 2025).

These gains stem from the fixed-sequence output of vision encoders and their ability to condense information-rich, multimodal content. The resulting architectures facilitate deployment in resource-constrained environments and open possibilities for further research in unified multimodal processing, where the distinction between “text” and “image” modalities becomes increasingly blurred.

6. Technical Formulations and Optimization Strategies

Across architectures, a variety of mathematical tools and losses underpin visual text representations:

• Losses: Mean squared error between predicted and ground-truth visual features, stochastic loss selection strategies, Pearson correlation maximization for similarity alignment, and contrastive InfoNCE-derived objectives (Carrara et al., 2016, Kurach et al., 2017, Grover et al., 2022).
• Attention and gating: Bilinear attention selectively aggregates text features with respect to visual context (Bai et al., 2017); multi-dimensional self-attention and attention-guided visual attention dynamically combine textual and image regions for robust multimodal NER (Arshad et al., 2019).
• Geometry-aware compositionality: Exponential and logarithmic maps on Riemannian manifolds (e.g., unit hyperspheres for CLIP embeddings, Lorentz hyperboloids for hyperbolic structures in MERU) (Desai et al., 2023, Berasi et al., 21 Mar 2025), e.g.:

$$\mathrm{Exp}_\mu(v) = \cos(\|v\|)\mu + \sin(\|v\|) \frac{v}{\|v\|}$$

or for hyperbolic distance,

$$d_{\mathcal{L}}(x, y) = \cosh^{-1}\bigl(-c\, \langle x, y \rangle_{\mathcal{L}}\bigr)$$

• Adapter modules and dynamic balancing: Residual bottleneck adapters reduce feature instability, while MCDB strategies compute per-modality gradient scaling to dynamically adapt optimization in joint spaces (Sun et al., 3 Sep 2025).

7. Design Implications, Human Factors, and Future Perspectives

Empirical and perceptual studies on visual text representations in human-centric contexts provide further design guidance. For instance, placing text “embedded” within graphics—rather than adjacent—improves memorability due to reduced split-attention effects, while using succinct, simplistic text increases pleasantness in infographic comprehension (He et al., 8 Feb 2024). Visual writing systems facilitate narrative editing by mapping and manipulating entities, events, and locations visually, promoting creativity and consistent story revisions (Masson et al., 9 Oct 2024).

Beyond performance, the adoption of visual text representations signals a paradigm shift toward unifying language and vision processing, circumventing artificial tokenization barriers, and modeling human-like reading and composition. Future directions proposed in the literature include scaling visual tokenizers to better suit large multilingual corpora, refining geometry-based embedding approaches for compositional reasoning, and exploring unified visual-centric models for all natural language.

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Visual text representations thus encompass neural mappings from language into visual feature spaces, fusion architectures for scene understanding and generation, vision-centric re-tokenization for efficient LLM input, compositional geometry-aware latent frameworks, and visual manipulation tools for both machines and humans. These advances collectively redefine how language can be encoded, compression achieved, and semantic alignment realized within multimodal AI.