Visual Granularity Sequence

• Visual Granularity Sequence is a structured progression through levels of detail, from fine instance features to broad semantic categories.
• Recent methodologies employ multi-stage deep learning and iterative refinement to fuse detailed and global representations for improved performance.
• Applications include visual search, fine-grained classification, interactive segmentation, and generative modeling, providing enhanced interpretability and robustness.

A “Visual Granularity Sequence” denotes an explicit, structured progression through different levels of detail or abstraction in visual representation, processing, understanding, or generation. This concept encompasses a range of methodologies—from hierarchical feature extraction and multi-stage training in deep learning models to adaptive user-facing visualization systems and iterative image generation regimes. Recent research leverages visual granularity sequences to improve interpretability, control, robustness, and efficiency in both discriminative and generative tasks. Below, the main dimensions are delineated with reference to empirical, algorithmic, and interpretive perspectives found across the field.

1. Granularity as Hierarchy: Structural and Semantic Design

Many visual recognition, search, and reasoning systems are constructed around the idea that images and their associated semantic concepts are organized in a natural hierarchy. This hierarchy spans from instance-level or fine details (e.g., particular patterns, object parts) up to increasingly abstract groupings—themes, classes, or semantic categories.

• In semantic metric learning for visual search, the “visual granularity sequence” is modeled via attribute semantic similarity. For example, visual similarity in the context of fashion search can range from the same clothing item, to similar style, to a generic category match. The SGML method (Manandhar et al., 2019) quantifies this multiscale similarity using an attribute space and integrates it into both network optimization and loss weighting.
• For fine-grained visual classification, layered network architectures (e.g., progressive multi-granularity training) (Du et al., 2020), granularity-aware CNNs (Song et al., 2021), and graph-based granular-ball representations (Shuyin et al., 2023) all explicitly or implicitly extract or propagate information across multiple levels in a structural hierarchy, yielding richer and more discriminative feature representations.

2. Methodologies for Sequence Construction and Utilization

The sequence of visual granularity may be constructed or traversed in several different algorithmic ways:

a) Progressive or Layered Processing:

Networks are organized such that features at each layer or stage correspond to a specific granularity. In (Du et al., 2020), the backbone is divided into multiple stages, with each extracting features at different scales, from local details up to global object structures, progressively fusing them for classification.

b) Attribute-Driven Sequence Modeling:

In metric learning (Manandhar et al., 2019), semantic attributes are used to define different levels of similarity, and the “semantic granularity similarity” (SGS) is computed (via cosine similarity in attribute space) to reflect the information provided by each image pair or triplet.

c) Iterative, Coarse-to-Fine Generation:

Generation models such as NVG (“Next Visual Granularity Generation”) (Wang et al., 18 Aug 2025) employ explicit staged refinement, starting from a global structure and progressively generating finer details in an ordered sequence. Each stage handles a constant spatial resolution but varies the number of unique tokens.

d) Adaptive and Controllable Granularity:

Systems such as GraCo for interactive segmentation (Zhao et al., 1 May 2024) expose the granularity control to the user, allowing explicit, parameter-driven specification of segmentation detail, reflecting the visual granularity sequence not just in the architecture but also in the human interaction loop.

3. Mathematical and Algorithmic Formalization

The formalization of visual granularity sequences has involved:

• Loss functions integrating the degree of semantic similarity (SGS) into metric learning losses, e.g.,

$$L_{pos} = \log [1 + \exp(-\alpha (s(a,p) + g(a,p) - \beta))]$$

where $s(a,p)$ is embedding similarity and $g(a,p)$ is SGS.

• Multi-level feature vectors and concatenation, e.g.,

$$V^{(concat)} = \text{concat}[V^{(L-S+1)}, ..., V^{(L)}]$$

for multi-granularity feature fusion (Du et al., 2020).

• Structured image decomposition,

$x = \sum_{i=0}^K a(c_i, s_i)$

in NVG (Wang et al., 18 Aug 2025), where $(c_i, s_i)$ denotes hierarchical content and structure tokens at stage $i$.

• Control parameter embedding for user-driven granularity, as in GraCo, with

$G = (1-\lambda)G_{scale} + \lambda G_{semantic}$

mapping the mask’s scale and semantic detail to the desired output (Zhao et al., 1 May 2024).

4. Empirical and Benchmark Findings

Empirical results consistently show that integrating multi-granularity or explicitly modeling granularity sequences leads to state-of-the-art or improved performance across a variety of domains:

• SGML achieves a +1–4.5% Recall@1 boost over strong baselines for in-shop visual search (Manandhar et al., 2019).
• Multi-granularity training in FGVC surpasses single-granularity/part-detection methods across CUB, Cars, and Aircraft datasets (Du et al., 2020).
• Granularity-aware models for interactive segmentation yield lower NoC@85 (fewer user clicks required for high-quality masks) compared to both single- and fixed multi-granularity competitors (Zhao et al., 1 May 2024).
• Fine-grained EEG-based classification and reconstruction benchmarks using multi-granularity labels highlight the brain’s stronger accuracy in coarse-level recognition, and the influence of label granularity on neural decoding (Zhu et al., 11 Jun 2024).
• NVG improves FID for class-conditional image generation relative to VAR at d = 16, 20, 24, confirming that structured, staged generation can outperform flat tokenization (Wang et al., 18 Aug 2025).

5. Applications Across Visual Domains

The sequencing of visual granularity is leveraged for a spectrum of applications, including but not limited to:

• Visual Search and Retrieval:

Semantic granularity sequences enhance image search/retrieval tasks by distinguishing between fine and coarse similarities, improving both the ranking and interpretability of retrieval systems (Manandhar et al., 2019).

• Fine-Grained Visual Classification (FGVC):

Progressive multi-granularity architectures outperform previous part-localization/ensemble approaches in differentiating among highly similar object categories (Du et al., 2020, Song et al., 2021).

• Interactive Segmentation and Annotation Tools:

Models supporting granularity-controllable mask prediction enable efficient, user-tailored annotation workflows for both part-level and whole-object segmentation (Zhao et al., 1 May 2024).

• Event or Sequence Visualization:

Visual analytics platforms (e.g., ICE (Fu et al., 2020), Sequen-C (Magallanes et al., 2021)) employ vertical and horizontal granularity adjustment (via clustering and column merging) for effective comparative analysis of large event datasets, facilitating multilevel exploration.

• Medical and Educational Video Parsing:

Datasets such as PhysLab (Zou et al., 7 Jun 2025) annotate procedural physics experiments at action, object, and interaction levels, supporting the development of intelligent educational technologies capable of fine-grained activity recognition and procedural error detection.

• Generative Models and Image Synthesis:

NVG’s staged, structure-content decomposition allows explicit, interpretable intervention in the generation pipeline, with demonstrated gains on ImageNet-scale generation tasks (Wang et al., 18 Aug 2025).

6. Broader Implications and Future Directions

The explicit modeling and sequencing of visual granularity have far-reaching implications:

• Interpretability:

Multi-granularity representations often allow post hoc or intrinsic explanation of model decisions, as attributions can be traced to specific granularity levels or semantic attributes.

• Robustness and Generalization:

Calibrating representations across scales, as in MGFC for domain generalized segmentation (Li et al., 5 Aug 2025), enhances robustness to domain shifts by aligning both global and local features.

• User Interaction and Adaptive Systems:

Adaptive granularity—driven by interaction provenance, user preference, or automatic detection—enables more targeted, efficient, and personalized analytics (e.g., health document exploration (Lengauer et al., 18 Feb 2025), AVG-LLaVA’s router for dynamic token selection (Lan et al., 20 Sep 2024)).

• Foundations for Multimodal, Multitask, and Neuro-AI Systems:

By decomposing high-level tasks into sequenced granularity levels, systems better mirror cognitive and neural processing—a trend evident in neuro-symbolic architectures (Tang et al., 2020), multi-modal LLMs (MaVEn (Jiang et al., 22 Aug 2024)), and neurophysiological benchmarking datasets (EEG-ImageNet (Zhu et al., 11 Jun 2024)).

The continued development and empirical substantiation of visual granularity sequence methodologies are advancing the state of the art in vision, multimodal, neuroscientific, and human-computer interaction research. The explicit, structured handling of granularity is emerging as a key principle in the design of next-generation interpretable, adaptable, and robust visual AI systems.