Learning AND–OR Templates for Compositional Representation in Art and Design
Authors: Liaoruxing Zhang
Xin Jin
Wenbo Yuan
ChenYu Fan
Song-Chun Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: AND-OR Template
Compositional Template Representation
Semi-Supervised Learning
Maximum-Entropy
Primary area: interpretability and explainable AI

Abstract: This work proposes a compositional AND–OR template for art and design that encodes the part–relation–geometry organization of images in a structured and interpretable form. Within a maximum-entropy log-linear model, we define a unified consistency score as log-likelihood gain against a reference distribution and decompose it into term-level evidence, enabling an evidence-to-prescription mapping for actionable composition guidance. Learning is performed by a penalized EM-style block-pursuit with sparsity and local mutual exclusivity: object templates are learned first and reused as scene terminals to induce scene templates. A semi-supervised structural expansion, which is triggered by matching gain and structural-consistency thresholds, bootstraps new branches from unlabeled, high-quality images. Evaluations on a curated compositional dataset and AVA/AADB themes show strong agreement with expert paradigms, interpretable parse trees, and competitive performance with deep baselines while exhibiting higher alignment with human ratings. The learned templates also act as lightweight structural conditions to steer AIGC generation and layout design. Overall, the framework delivers a transferable structural prior with favorable data/parameter efficiency and a unified pathway for explainable visual assessment and generation.

Published as a conference paper at ICLR 2026
LEARNING AND-OR TEMPLATES FOR COMPOSI- TIONAL REPRESENTATION IN ART AND DESIGN
Liaoruxing Zhang, Xin Jin; Wenbo Yuan & ChenYu Fan Department of Cyberspace Security Beijing Electronic Science and Technology Institute
Beijing, China 2hangxingxing 028 com, jinxinbesti@foxmae com
Song-Chun Zhu
Beijing Institute for General Artificial Intelligence Institute for Artificial Intelligence, Peking University Beijing, China
ABSTRACT
This work proposes a compositional AND-OR AID-OR template for art and design that encodes the part-relation-geoneee organization of images in a structured and interpretable form. Within maximum-entropy log-linear model, we define a uni- fied consistency score as log-likelihood gain against reference distribution and decompose it into term-level evidence, enabling an wvidence-to-prescripe ping for actionable composition guidance. Learning is performed by penalized EM-style block- pursuit with sparsity and local mutual exclusivity: object tem- plates learned first and reused scene terminals induce scene templates. A semi-supervised structural expansion, which is triggered by matching gain and structural-cconsisteeey thresholds, bootstraps new branches from unlabeled, high- quality images. Evaluations on a curated compositional dataset and AVA/AADB themes show strong agreement with expert paradigms, interpretable parse trees, and competitive performance with deep baselines while exhibiting higher align- ment with human ratings. The learned templates also act as lightweight structural conditions to steer AIGC generation and layout design. Overall, the framework delivers transferable structural prior with favorable data/parameter efficiency and unified pathway for explainable visual assessment and generation.
map-
INTRODUCTION
Art and design are markedly structured. Existing tools for visual assessment and creative assistance often rely on surge-scale annotations and black-box representations, making it hard to turn the ques- tion of why something is good, and how to improve it, into a traceable, actionable chain of evidence (Murray et al. 2012 Simonyan & Zisserman, 2014 He et al. 2016, Dosovitskiy et al. 22200). They also struggle with cross-scene transfer and few-shot generalization. structured representation: reusable parts and constraints that capture how visual elements ganized in art and design, so a model can Iearn "general structure" while producing executable, task-specific recommendations.
advocate a recomposable
We propose compositional representation based on AND-OR templates for art and design (Si & Zhu 20133] Zhu et al. 2007). AND nodes encode relational and geometric constraints that must be satistied jointly; OR nodes represent alternative structural variants; terminals are observable primi- tives in and design with statistical attributes. Under maximum-entropy log-linear framework, consistency as the lig-likelihood gain relative to reference distribu-
we define amage-templee tion. yielding a unified scorer. The scorer decomposes cerm-wise into object, relation, and geometry evidence, which naturally maps to "evidence - prescription" style adjustment suggestions.
"Corresponding author.

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Learning and Verification
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Tempins
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Figure I: Overview of the compositional AND OR template framework (top: Learning & Verifica- tion; bottom: Exploration). Top: Learn AND OR templates from professional photos; them for consistency scoring, parse-tree guidance, and as structural conditions for AIGC. Bottom: Clustering and semi-supervised expansion yield new theme templates.
For scalable learning, EM-type block-pursuit algorithm: in the E-step we perform terminal matching, relation instantia- tion, and geometric evaluation; in the M-step add OR branches refine constraints by penalized marginal gain, while local mutual exclusivity and sparsity regularization suppress combinatorial ex- plosion. Learning proceeds two levels: we first obtain reusable object templates, then reuse them as scene terminals to compose scene templates. We further introduce a semi-supervised extension: dual threshold of matching gain structural consistency bootstraps structural increments from unlabeled data, with conflict resolution and early stopping for robust expansion.
structure induction as penalized maximum likelihood and employ
We use a licensed collection of professional photographers' works as small-sample seeds and an unlabeled expansion pool: the former initializes object/scene template learning, the latter supports semi-supervised growth. On this basis, we validate the method as representation-leamiin work, including downstream applications: photography guidance, AIGC generation guidance and film-poster layout design, used respectively for framing suggestions, re-ranking generation results, and layout optimization with the template score the objective. We also evaluate cxisting thetic corpora like AVA and AADB (Li et al. 2010).
frame-
The main contributions of our paper can be summarized follows:
We extend AND templates from the object level to the scene level. Within a maximum-entropy log-linear model we define unified consistency score (log-likelihood gain) that supports training selection and test evaluation, and that decomposes into object relation geometry terms.
Compositional representation and unified scoring:
Penalized MILE with EM-type block-pursuit: We instantiate learning penalized maxi- mum likelihood with an EM-type block-pursinn procedure adapted to compositional induc- tion. Local mutual-exclusivinn and sparsity regularization curb combinatorial growth, and two-level reuse (objects - scenes) improves parameter efficiency.
SSE: semi-supervised structure expansion: A dual-threshold rule (matching -aiin + structural cnnetural-consistennyy bootstraps new branches from unlabeled images, with conflict res- olution and early stopping for robustness.
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Faithful interpretability and prescriptive guidance: The score's berm-wise factorization provides cisenneeto-decisiie attribution; parse graphs visualize activated terminals and (dis)satisfied constraints and translate attributions into actionable structure/geomeety edits.
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RELATED WORK
2.1
COMPOSITIONAL AND GRAMMAR -AASE VISION
A long tradition explains images by parts and their relations. Classical formulations include de- formable templates (Yuille al. 1992). Active Appearance Models (Baker et al. 2004), pictorial
structures (Felzenszwalb & Huttenlocher 2005), constellation models (Fergus et al. (2007). hierar-
chical parts dictionaries (Fidler & Leonardis (007), and Active Basis (Wu et al. 2010). Discrim- inatively trained part-based detectors (DPM) established that coupling appearance with geometric deformation is highly effective for robust detection (Felzenszwalb et al. [2009]. Beyond single cat-
egories, taxonomies and recursive compositionality share parts/appearance across classes and views for better sample efficiency (Ahuja & Todorovic 2007 Detection et al. 2010).
Within grammar- grasedr-bbaeed modeling, Hybrid Image Templates (HIT) integrate sketch primitives with texture/fattnesscoloorr cues and are learned by information projection (Si & Zhu 201T). The AND- OR Template (AOT) makes reconfigurability explicit by separating AND-nodes (joint composi- tion/constraints) from OR-nodes (structural/geometric alternatives), yielding a template that enu- merates valid configurations while remaining interpretable (Si & Zhu 2013). From a broader the- oretical standpoint, stochasticcattributed image grammars provide a top down/bottom-up parsing view in which terminals contribute local evidence and higher nodes regularize legal structures (Zhu jet al. 2007 Liu et al. 2014 Park et al. 2017). In parallel, representation learning evolved from deep backbones (VGG, ResNet) and attention mechanisms (SE, 2014 He et al., [016]] Hu et al.] 2018 Dosovitskiy et al. (020);; while these models excel in accuracy, their internal reasoning is typically opaque at the composition
HOG (Dalal & Triggs 2005
ViT) (Simonyan & Zisserman
level.
From objects to scenes. Compared with prior AOT/HIT usage that primarily targets object cate- gories, we extend AOT from the object level to the scene level. Concretely, learned object templates are reused as scene terminals and composed via AND/OR operations under explicit inter-object con- straints, so that scene regularities (e.g., relative placement, scale compatibility) are modeled without proliferating low-level parts. This design aligns with attributed grammar- based scene parsing (Liu) ct al. 2014) and is compatible with object-relation attribute euiittrrelaionnnattibbuttee structure widely adopted in scene un- derstanding corpora (Krishna et al. 2017] Xu et al. 2017). The result is a two-level, interpretable representation in which object knowledge transfers upward structure scenes.
2.2 STRUCTURE-AWAR SCORING AND UNIFIED LEARNING
Structure-aware quality modeling progressed from subject/semantic cues (uoo & Tang 2008) to large-scale learning setups (e.g., AVA) that support quantitative evaluation and correlation with hu- man preference (Murray et al. 2012). and to explicitly reasoning about recurring compositional pat- terns (Nguyen et al. 20118). Our framework is theory-leaning: under a maximum-entropp log-linear model, we define a unified consistency score as log-likelihood gain against reference distribution. The score decomposes into object, relation, and geometry terms mimiroiii the grammar so that positive/negative evidence aggregates transparently to final rating. This view connects to classi- cal maximum-entropy feature binding and Markov random field modeling (Zhu et al. 1998), while remaining faithful to modern compositional parsing.
Methodologically, we pursue scalable structure induction by penalized estimation with sparsity and local mutual exclusivity, alternating between bottom-up terminal matching and top-down constraint refinement. The two-level reuse (objects - scenes) curbs combinatorics, and the resulting consis- tency score provides term-wise attribution that can be used for principled selection, comparison, and ablation. In contrast to black- predictors (Simonyan & Zisserman 2014 He et al. 2016 Dosovitskiy et al. 2202)), the proposed family preserves explicit part part- geometry factors and yields auditable path from evidence decision.
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Scene Template
Geometric OR
Position
Size
Color
Object Templates
Structual
Geometric
Figure 2: Detailed illustration of the compositional AND-OR template. OR nodes enumerate struc- tural variants and geometric transforms; AND nodes (solid blue circles) encode joint satisfaction of their children; terminal nodes correspond to observable features.
3 COMPOSITIONAL TEMPLATE REPRESENTATION
3.1 TASK AND DATA
We use small authorized set of professional photographs as seeds and maintain an unlabeled pool for sum--vuper expansion. The goal to induce compositional (reusable) structured tem- plates from thematically coherent image collections, and to derive unified scoring rule as well as evidence-to-presriiioo suggestions. Data provenance and statistics are deferred to Appx./A.]
3.2
COMPOSITIONAL. AND-OR TRMPLATES AND UNIFIED SCORING
3.2.1 TEMPLATE STRUCTURE
We adopt two-level representation composed of object templates and scene templates. At the object level we use an AND-OR graph: AND nodes encode relational and geometric constraints that must be satisfied jointly; OR nodes capture structural and geometric variants; terminals ob- servable primitives with statistical attributes. At the scene level, learned object templates are reused "scene terminals" and combined via AND-OR compositions encode inter-object constraints and equivalent variants. As an illustrative example, consider "river surrounding mountains" scene with four main objects: river (B), middle mountain (A), left mountain (C), and right mountain (D). The root is an OR node that enumerates valid scene configurations: its children are AND combi- nations of structural and geometric settings for each component. Further down the hierarchy, the middle mountain and the river can be split into left/right segments, and the left/right mountains into upper/lower segments. These segments serve as terminals object templates, whereas scene templates treat object templates terminals to avoid configuration blow-up if tiny segments were elevated to scene terminals. Fig. [ visualizes the structure: terminals (light blue rectangles), AND nodes (solid blue circles), and OR variants (dashed boxes).
Both levels are regulated by a stochastic context-free grammar (SCFG) to compactly model struc- tural & geometric variability and higher-order part interactions. Terminals represented Pho- tography Art Templates (PATs), consisting of image primitives and histogram descriptors (c.g., color/texture), each carrying position, scale, and orientation attributes.
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A PAT can be specified by list:
PAT {(B1, 41, y1, $1, 01), (h2, 22. 12). (h3, T3.J3). (B1, 24, Ut. S4, 01). ...
where B1, B4, are image primitives and hzz, ha, are histogram descriptors for texture, flatness and color. (I.j yÿ) denote the selected locations and 0j the selected orientations.
3.2.2 UNIFIED SCORING
Let X+ (T,,... IN. J be positive images drawn from target distribution /(T) and let g7)) denote reference distribution over generic natural images. Starting from we progressively reweight by selected features toward f.
pr( ( = Po(1) - p1() + - p(I) = f(I).
(1)
Under the maximum-entropy principle, the model admits log-linear form
T
p(1)= 9(1) t=1
II exp(
(2)
where 3, parameterizes feature rr. and 2, = Σr, ((r)) ( the normalizer. Given a tem-
plate Temp and configuration (s. g) (structural and geometric), the complete likelihood
p(,,s, s; g Temp, 3) = p(s, g Temp) p(1 s. s.
(3)
with the conditional likelihood
K Sk Σ
) AOO)
p(1 s, B) exp
(L) - log Ze
((1)
(4)
Here D is the number of candidate features and K is the number of template terminals, / is the parameter for the t-th selected feature rt and 2n (2, 2 0) the individual normalization constant determined by B. q(I) represents the reference distribution.
We define the template matching score, also known as the consistency score and the information gain, the log-likelihood ratio.
Score(T) = loog ((T|8,
K Scoro(PATss)
(5)
9(1)
where each activated terminal contributes
Score I) = Sk Σ
(1) log Zk
(6)
This additive decomposition provides direct mapping from evidence to prescription: posi- tive/negative evidences at the part or relation level translate into actionable geometric or structural adjustments.
4 LEARNING COMPOSITIONAL AND-OR TEMPLATES
This section details the EM-type block-pursuit procedure for learning AND-OR templates under the unified scoring and log-linear modeling introduced earlier. The learner alternates between esti- mating latent structural and geometric configurations (E-step) and performing penalized maximum- likelihood updates of structure and parameters (M-step). We first learn object templates and then reuse them scene terminals to induce scene templates. Finally, extension that enlarges structure from an unlabeled pool via a dual-threshold criterion (matching gain and structural consistency) with conflict resolution and early stopping.
describe semi-supervised

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4.1 EM-TYPE BLOCK-PURSURE
4.1.1 TRACKING MATRIX (B-STEP)
We begin by applying YOLOv11 to detect and segment objects, obtaining object-level training samples. For each sample, candidate feature responses are computed form the data matrix R RNXD, where each entry is Ra (1.) E [0, 1]. The E-step, given the current template and parameters, performs terminal matching. relation instantiation, and geometric evaluation. Con- cretely, within local windows conduct finite search over translation, scale, and rotation; per- terminal contributions are evaluated using the additive matching score, and only combinations that satisfy the node celationl//coometr constraints retained. The E-step outputs sample-wiss wise activated blocks and their poses, which serve as sufficient statistics for the M-step.
0.9
0.8
0.8
0.7
1.0
0.8
0.6
1.0
1.0
0.8
0.6 0.3
0.7
1.0
0.7
1.0
1.0
0.5
1.0
0.8
1.0
0.6
0.4
Figure 3: Initial Data Matrix. Data matrix is matrix with N (number of positive examples) rows and D (number of all candidate features) columns, and each entry R.j rr,[,) is a feature response (0 S R,j $ 1). Larger value of R, means feature j appears in image I; with higher probability.
4.1.2 PENALIZED MARGINAL GAIN (M-STEP)
On R, we pursue large blocks [ comprised of many high responses; they correspond to fre- quently occurring, high-confidence PAT,, A block is specified by set of shared features (columns) over subset of examples (rows). Its significance is measured by the summed score inside the block:
Score Bx)
Σ
(Bx. Rag log )
iErows( B&) jEcola(Bs)
As in the main text, cols (B) are the selected features in PAT rows (B) are the examples on which PAT, is activated, and žı. follows the normalization used for the log-linear form. Maximizing the total block score corresponds to penalized maximum-likelihodd likelihood estimation; we minimize a two-term objective
L,R,B,s,, penalty((8)
with sparsity and local mutual exclusivity suppress overlap and redundancy. In practice, (i) 3k, is constrained within local object windows to avoid cross-region leakage; (ii) per image and per local region, most one block allowed be active; and (iii) low-scoring blocks are pruned after ranking. Initialization uses high-frequen terminals and stable local relations. We stop when the marginal information gain of the newly proposed block falls below a threshold, or when both structure size and validation score improvements below for several rounds. The end-to-end pipeline is shown in Fig.
Algorithmic overview. For completeness, we provide the full pseudocode of our learning and inference procedures in the Appx. Algorithm[Jdetais the EM-rype block-pursuit procedure used to learn the compositional AND-OR templates from the response matrix It under the log-linear model in Sec. obtain the structural configuration geometric configuration g. and the decomposable consistency score. The main text reports high-level intuition and ablations; the Appendix gives all steps needed reproduce results.
Algorithm 2 specifies the recursive SUM-MAX inference used at test time to

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Training images
Image preprocessing
Learning Object Templates
Object Templates
Learning Scene Templates
Iteration
Vasaliation
Moan Value of Template Configuration Foatures
Figure 4: End-to-end lemplate-learning pipeline. Images are first preprocessed by an object detector and segmenter (e.g.. YOLOv 11). EM-type block-pursuit algorithm then learns terminal and non- terminal nodes from the data matrix R. Learned object templates are composed into scene templates via the same procedure, and mean configuration features are aggregated for visualization.
4.2 TWO-LEVEL LEARNING PIPELINE
Object template learning. On the object-level matrix R, EM-type block pursuit iteratively selects high-scoring blocks to instantiate terminals and con-terminals and estimate the coefficient matrix ((T), Sparsity and local mutual exclusivity control model complexity; blocks ranked by infor- mation gain and truncated, yielding a compact and interpretable AND-OR structure at the object level.
Scene template learning. In the object-level feature space, we construct R' E R' where each row corresponds to a photograph X+ and each column candidate object-level feature, with entries R' (4) E [0, 1]. Treating learned object templates scene terminals, the same EM-type block pursuit R': the E-step emphasizes inter-object relations and global geometric consistency, while the M-step, under penalties, decides whether to add new OR branches refine cross-object AND constraints. This two-level scheme reuses object knowledge at the scene level and avoids unnecessary pixel-level combinatorial search.
apply
4.3 SEMI-SUPERVISED LEARNING OF COMPOSITIONAL AND-OR TEMPLATES
This paper extends the compositional template learning method into a semi-supervised framework, exploring the capability of algorithms to autonomously derive templates from unlabeled image data. This approach enhances learning efficiency while providing novel insights into the semantic depth and aesthetic principles of images. Building upon the preliminary aesthetic template construction, we analyze the relationship between the number of nodes in the template graph and the training image volume, aiming to evaluate model performance and sensitivity to data variability comprehen- sively.
Additionally, assess the robustness of semi-supervised learning in the face of disturbances and anomalous data, examining its impact on model stability. Such analysis reveals the algorithm's 's adaptability complex environments, offering more resilient and efficient solutions for image pro- cessing. Through systematic experiments and analysis, this study aims to provide a richer theoretical foundation for compositional template learning, contributing both advancements the practi- cal application of these algorithms in real-world contexts.For detailed information, please refer to Appx. A.2
EVIDENCE AND VALIDATION
We evaluate the proposed compositional AND-OR template along three dimensions cimeessonn--eliailliii interpretability, and performance/data efficiency across tasks. The results show that the template
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Templates Summarized by Photographer
Training Images
Templates by
by Midjourney by Template
Visual Result
Matching Score: 0.837
Visual Result
Matching Score: 0.862
Figure 5: Reliability verification of compositional templates. Expert- Summr--sumarrized templates are compared with templates learned by our method, and effectiveness is further checked using training and generated exemplars. High matching scores correspond to structures preferred by professionals, supporting the reliability and applicability of the learned templates.
consistently captures human- recoanizee aesthetic regularities, enables explainable predictions of image quality, and achieves performance comparable to or better than deep learning baselines on classification tasks.
5.1
CONSISTENCY AND RELIABILITY OF THE COMPOSITIONAL AND-OR TEMPLATE
5.1.1 OBJECTIVE CONSISTENCY
Under expert guidance, we curated six representative compositional structure paradigms and, for each paradigm, selected 30 high-quality images to learn theme template. The learned templates show strong agreement with the expert paradigms in both structural and geometric constraints, indicating that the model automatically distills the same compositional regularities. We quantify alignment using the consistency score defined in equation -he log-likelihood gain relative to reference distribution-ann observe scores a high range, supporting structural consistency. As controls, report two perturbation studies: (i) counterfactual templates obtained by part substitu- tion or relation shuffling, and (ii) geometric jitter by scale and position perturbations. Both controls lead to marked drops in the consistency score. demonstrating sensitivity to structural and geometric disruptions. To assess structural controllability. We used the learned templates as prompts for an AIGC image generator (e.g., Midjourney); As shown in Fig the outputs adhere to the prescribed part relations and geometric layout, showing that the encoded constraints are executable, rather than mere style reproduction.
5.1.2 SUBJECTIVE RELIABILITY
We further conduct a user study with 100 domain practitioners on randomly selected theme tem- plates. Using five-point scale, participants rate the structural validity and theme alignment (fully consistent, mostly consistent, average, inconsistent, completely inconsistent). As shown in Fig II the majority report agreement with professional knowledge: 74.8% fully or mostly consistent, and about 12% inconsistent. Several participants note that the templates capture the layout and inter- element relations emphasized in practice. These subjective results align with the objective analyses and indicate that the learned templates provide a structure prior consistent with human understand- ing.
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Table I: Quantitative comparison AVA with single-image input methods.
Method
Accuracy (%)
Number of Parameters
Training Complexity
SRCC
Ours
85.65
2.3 10
6.7 10a
0.8419
VGG19 (2-class)
78.08
1.39 x 10
1.96 x 10th
0.7506
ResNer50 (2-class)
85.71
2.35 x 10?
1.09 x 10"
0.7842
VIT- (2-class)
85,38
8.58 X 107
1.75 10tH
0.7590
VGG19 templates
81.43
1.39 x 10
1.96 10
0,7893
ResNer50 + templates
85.87
x 107
1.09 x 10
0,8156
VIT-B/16 + templates
86.20
8.58 X 107
1.75 10"H
0.7855
5.2 INTERPRETABILITY AND CONSISTENCY SCORE WITH COMPOSITIONAL AND-O TEMPLATES
For interpretable scoring and parsing, given theme template and target image we compute the consistency score to measure their structural alignment and produce parse tree for the image. The parse explicitly marks activated terminals and the satisfied or violated relational/geometrie con- straints. According equation [5] the score decomposes into object-, relation-, and geometry- contributions, yielding a clear attribution pathway: satisfying key parts and constraints increases the corresponding contributions and the overall score; missing required parts or violating constraints suppresses activations and reduces the score. Fig. [2] shows typical cases: the template structure (left) and image parses with scores (right). This mechanism supports auditable explanations and provides structured repair suggestions for downstream idence, together with interpretable outputs, substantiates our central claim: compositional AND-OD templates reliably capture human-rec recogniee structural regularities, yield perturbation-seesitiie decomposable consistency score, and act as executable structural constraints for downstream sys- tems.
Overall, the objective and subjective
To further validate the effectiveness of the template scoring, we compared the model prediction with manual subjective evaluation. For detailed information, please refer the Appx.
5.2.1 PERFORMANCE EVALUATION AND BASELINE COMPARISON
We evaluate the compositional AND OR template structured, interpretable classifier on AVA. The AVA labels serve proxy supervision to assess the discriminative power and inductive bias of the representation. For each of themes, we select 140 high-score and 140 low-score images to learn two theme templates. At test time, we compute template and predict the class with the higher score. The score is a real-valued alignment metric defined by equation 5 as the log-likelihood gain relative to reference distribution. It supports ranking, correlation analysis, and thresholding.
image's consistency score with each
Baselines include VGG19 Simonyan & Zisserman 2014), ResNet He et al. (2016) and ViT-B/16 Dosovitskiy et al. (2020). We also perform late fusion by combining template configuration features with deep features test complementarity. Metries are Accuracy FLOPS (floating- poonggg--oontttoo operations, as proxy for computational cost), and Spearman rank correlation (SRCC) between the consistency score and the AVA annotation scores.
Table shoos that, with substantially less data and far fewer parameters, the compositional template achieves accuracy comparable strong baselines. Late fusion further improves the deep networks. The consistency score attains higher SRCC with human annotations, indicating stronger alignment between the structured representation and the label standard. Importantly, under the log-linear for- mulation the score decomposes into object-, relation-, and geometry-level contributions, yielding clear attribution pathway that links structural evidence the final decision. This demonstrates favorable trade-off among discriminative power, data/parameter efficiency, and interpretability.
Other experiments In addition to the above verification, we also conducted other objective and subjective experiments, please refer the Appx. for details

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LIMITATIONS AND CONCLUSION
We introduce a compositional AND-OR template with a maximum-entropy log-linear unified score, learned via penalized MLE with an EM-type block-pursuit and semi-supervised structural expan- sion across object and scene levels, achieving competitive performance with strong baselines while preserving interpretability and actionability. Limitations include subjective aesthetics and limited scale blurring theme boundaries, dependence on initial templates and expert curation, lack of sys- tematic cultural/domain tests, coverage of parts/geometry but not lighting/material/pporiie factors, hyperparameter sensitivity in the cual--treshold semi-supervised stage, and prototype-level application modules. The impact an evidence-to-pe topee--erttttiiiinnnn structured paradigm with few-shot data efficiency and transfer, tempered by risks of stylistic/cultural bias and "templatized" creation. Future work will expand and stratify data, incorporate higher-level factors, strengthen evaluation and workflow integration, and conduct larger-scale cross-domain validation.
ETHICS STATEMENT
This work studies compositional representations for art and design. No personally identifiable in- formation is used. The curated datasets (e.g., AVA/AADB and licensed professional photographs) used under their respective terms; for scanned third-party images we obtained permission used publicly available items with proper rights, and we release only non-identifying derivatives. Our user evaluation (expert preference ratings) was conducted with informed consent and without storing personal data; when required, institutional approval was obtained.
REPRODUCIBILITY STATEMENT
We provide implementation details for the model and learning procedure (Secs, including the scoring definition (Eqs. ( search ranges, and penalties. Dataset composition and splits described in Appx. A. semi- semi-supervised settings in Appx. A.2] Additional plots are included in the A.7 An anonymized code package and instructions are supplied supplementary materials.
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A
APPENDIX
A. COMPOSITIONAL IMAGE TEMPLATE DATASET (CITD).
To support compositional representation and AND-OR template learning in art and design, rate the Compositional Image AND-OR Template Dataset (CITD). The dataset is built from public sources (e.g., AVA, AADB) and expanded with images collected from professional photogra- phy websites, works by renowned photographers, and scans from photography monographs. Images grouped by compositional struclure paradigms- for example, "U-shaped bay". "boardwalk", "flying eaves", and "skyscraper atrium patterns" "- so that parts, relations, and geometric constraints can be learned and evaluated the theme level.
CITD contains 750 images across 15 compositional themes, with 50 images per theme (see Fig. oor examples). For each theme, we designate 525 images as structure exemplars that clearly instantiate the target composition, and 225 images as non-exemplar controls sourced from popular image- sharing platforms and web search results under the same theme tags. The dataset provides theme- level labels only (no pixel-level annotation), and is intended to (i) seed AND-OR template learning with positive examples, (ii) supply controls for calibration and robustness, and (iii) offer a unified basis for consistency scoring and downstream evaluations.
Skypcraper
U-shaped
Beardwalk
Figure 6: Partial display of our Compositional Image Template Dataset.
A.2 DETAILS OF SEMI-SUPERVISED LEARNING
A.2.1 TRAINING DETAILS
As established aesthetic datasets, the AVA and AADB datasets contain a wealth of multi-theme and
multi-dimensional aesthetic images, making them highly suitable as foundational data sources for the automatic discovery of templates (Murray al., 2012. Li et al. 2010). This section proposes a semi-supervised template learning method to learn new image templates on the AVA and AADB datasets.
First, we manually select aesthetic images of the same theme from the datasets to construct the corresponding initial templates, serving as the starting point for semi-supervised learning. Next, the algorithm automatically selects images from the dataset for learning. When faced with training samples of a new theme, the algorithm first attempts to interpret them using the existing templates (Si & Zhu 2013 Wu et al. 2010). If the matching score between the existing templates and the new images is low, we integrate the new configurations and update the existing templates. By iteratively refining this process,
gradually enrich the content of the initial templates, ultimately forming a compositional AND OR template graph that includes multiple templates. The detailed workflow
of the semi-super learning process is illustrated in Fig. 7
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photography template
Learning new training
contiguration photographt template
match
New photography Samplades
Random
AUA AADS
Figure 7: Semi-supervised Compositional AND-OR OR Template Learning Process. Starting from initial template, the algorithm autonomously selects images from the AVA and AADB datasets for further learning. When new thematic samples are encountered, the algorithm attempts match them with existing templates; if the match score is low, new configurations are added to the existing tem- plate to update it. Through iterative refinement, the content of the template is enriched, eventually forming compositional AND-OR graph encompassing multiple styles. This process enhances the diversity of the templates while improving the flexibility and efficiency of aesthetic analysis through semi-supervised learning.
Templates obtained from seml-supervised learning
Training samples automatically found by model from AVA and AADB dataset
sky
bay
sky
sea
spray
spray
sky
mountain
Inverted image
mik
lake
Figure New templates obtained from semi-supervised learning.
Numbe of nodes
50
100
150
200
250
300
350
Total number of training images
Figure 9: The relationship between the number of training images and the growth of the number of nodes.
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A.2.2 PERFORMANCE ANALYSIS
Compared to supervised learning methods, semi-supervised image aesthetic template learning faces larger and more complex volume of data, and autonomous learning implies varying quality of the acquired training data Fergus et al. (2007). Therefore, this section primarily focuses on two aspects of performance evaluation: first, the growth of the template AND-OR graph when handling large datasets; and second, the robustness the learning method when confronted with unrelated training data. The former reflects the capacity of the learning method to handle substantial influx of data autonomously, while the latter demonstrates its resilience against anomalous or noisy data.
In analyzing the growth of the AND-OR graph under semi-supervised template learning, amined the relationship between the number of nodes the template AND-OR graph and the total number of training images. shown in Figure / The results indicate that the number of nodes in the graph exhibits sub-linear growth, suggesting that many nodes are highly replicable and can be shared across multiple subgraphs. By employing the methodologies constructed in Secs. 4 can automatically optimize these redundant nodes to effectively address the challenges posed by large-scale training data Simonyan & Zisserman (0014).
To validate the robustness of the semi-supervised learning method, this section designs a cross- validation experiment. First, two independent training samples of size n are selected from the same thematic training dataset, and corresponding templates are learned. Subsequently. the KL divergence calculated on third independent sample the same theme (test data) to assess the differences between the two templates. The specific calculation method is as follows:
pbb;TMP
K(TMP R Σ log P(b;TTP))
where TMP* and TMP represent the two trained templates. A smaller calculated KL divergence value demonstrates a lesser degree of difference between the two templates, thus reflecting the liability of the semi-supervised learning algorithm under random training data Baker et al. (2004). We repeatedly observed the changes in KL divergence for different sample sizes n, with the results illustrated in Figure I0 The experiments demonstrate that when reaches 100, the KL divergence of the templates obtained through semi-supervised learning can be reduced to 0.1, indicating good robustness.
0.5
0.4
KL dispersion
0.3
0.2
0.1
2
3
4
Training data size
Figure 10: Changes in training data size and KL divergence.
A.3 HUMAN ALIGNMENT
To assess whether the compositional AND-OR templates provide structural representation aligned with human judgments, we conducted a pairwise- preference study. We randomly sampled 50 im- ages to form set P. For each image P E P, we paired it with every other image in the set, yielding
pairwise-preference
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2,500 binary comparisons completed by 15 raters with art/design backgrounds. Pairwise com- parison, rather than absolute scoring, is more in line with expert practice and more stably reflects structural differences.
For each image P, let the binary variable Hr,J E {0, 1 indicate whether P is preferred over J. We estimate the win probability
(P) = [ wins against J}},
(8)
and fit a logistic/Bradle--Tens model to the win-loss outcomes to obtain a latent human score d(P) E (0,
logit do + IATTR(P),
where r& (P) can be aligned with the template's consistency terms (e.g., object-, relation- and geometry-level cues).
We then compare ú(P) with the consistency score S(P) defined in equation 5 the log-likelihood gain relative to reference distribution, normalized to [0. 1n]aadd measure proximity via mean squared error (MSE). As shown Fig. (dashed line indicates ideal alignment; MSE 0.0286), the consistency score exhibits stable ordering consistent with human judgments, with small devi- ations.
These results indicate that, within the structural paradigm of art and design, the unified score pro- duced by compositional AND-OR templates aligns well with human assessments of part-relation- geometry organization. Overall, this study validates our representationn-soriig framework from human-alignment perspective art and design settings.
A.4 STRUCTURE-AWARE ASSESSMENT AND GUIDANCE FOR PHOTOGRAPHYY
In the field of photography, professional photographers often find commonalities and extract re- peated structural patterns from a series of photographs with the same theme, thus forming some "template" techniques for photography. Beginners in photography can refer to the "templates" sum- marized by these experts, imitate shooting, and add their own thinking on this basis to create better works. We use a compositional AND-OP template to achieve this operation, aligning the input photo with the theme template and resolving issues such missing objects, poor scene size and orientation resulting in poor composition in the photo. At the same time, the output includes both textual and visual actionable guidance, guiding photographers how to improve on the shortcom- ings of their photographic images.
As shown in Fig. 13 have designed rule corpus that will output corresponding guidance rule statements based on the comparison results of two templates. At the same time, it will also output combinable AND-OR images, interpreting the image by indicating which part is activated. Finally, provide corresponding guidance on the original image based on the visualization results of the template.
A.5 CEMPLATEE-ONSTRAAEE
POSTER GENERATION
We further test structural controllability in AIGC workflow for film posters. From poster exam- ples, we learn a theme template and use it as conditioning signal for a general generator. The goal is enforce part relations and geometric layout specified by the template. As shown in Fig. 14 the generator produces plausible posters that respect the prescribed structure. We also note cur- rent limitations: industrial poster design involves complex pipelines, and today's generative models offer limited controllability. Our results are proof of concept that the learned template acts as lightweight structural constraint for creative generation.
A.6 SCENE CLASSIFICATION WITH AND-OR TEMPLATES
To examine transfer beyond aesthetics, apply the templates to classification.
CITD evaluation. On a 15-theme dataset, we perform one-to-all tests with 5x5 cross-validation, comparing against HoG Dalal & Triggs (2005) linear SVM, part-based SVM, and pretrained
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ResNet. As shown in Fig. 15 the template approach outperforms traditional small- sample baselines and approaches the pretrained deep model, while providing parse-level explanations unavailable to black-box classifiers.
Places365 subset. We select categories, learn category templates, and rank test images by their template consistency over the 10 templates make predictions. Table reportss Top- 1op-1/Top-5 errors. The template method achieves competitive Top-1 and strong Top-5 performance compared to stan- dard deep baselines, far lower data and parameter costs and with inherent interpretability. These results indicate favorable trade-off among accuracy, efficiency, and explanation for compositional AND-OR templates on a general vision task.
Table 2: Scene classification 10-class subset of Places365. Errors are in %.
Method
Top-1 I Error
Top-5 Error
Ours
23 23.44
8.03
Places-365-CND Zhou et al. 2017
23.27
8,48
ResNet He et 2016
23.35
8.61
SENet Hu al. 2018)
23.67
8.26
50.00%
43.60%
40.00%
31.20%
30.00%
20.00%
13.20%
10.00%
6.90%
5.1%
0.00%
Fully consistent
Basically consistent
Average
inconsistent
Completely inconsistent
Figure 11: Subjective evaluation of compositional template reliability.
A.7 OTHER QUALITATIVE EVIDENCE AND PROCEDURAL DETAILS
This section complements the quantitative results with qualitative evidence and end-to-end method- ological details. Algorithm | specifies the EM-type block-pursule procedure used for penalized structure induction at both the object and scene levels, and ( the recursive SUM- MAX inference that yields the structural configuration, geometric pose, and decomposable con- sistency score at test time. Figures 19 visualize (i) the consistency score and its attribution over part/relation/gcoeeee metric perturbations, and (iii) representative outputs including parse graphs, activated terminals, (dis)satisfied constraints, and prescriptive edits. Together, these materials make the learning dy- namics and the vridence-to-decision pathway explicit, and facilitate reproduction and diagnostic analysis.
terms, (iI) reliability checks under counterfactual structural and geo-
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Best
Esplainable Photo and Languaga
Photography Guidance
Jiy
0.870
Cepellageu
Worst
serind and fooeed lo
0.345
Figure 12: Quantitative assessment results. Besides reporting the consistency score, the method visualizes the parse configuration. For low-scoring images, interpretable guidance is provided by contrasting the parse with the template.
Algorithm EM-type Block- Learning of Compositional AND-OR Templates
Require: Response matrix It E 10,1 (rows: examples, cols: candidate features), initial struc- tural indicator ( (optional), maximum #blocks K, regularization parameters Amx (mutual- exclusion) and Asp (sparsity), tolerance or feature budget.
Ensure:
Coefficient matrix kKxD, normalizers E 1B KDD and learned structural indicator
1: Initialize 0; set ((0) 00, ( t block feature sets s + 0 for h = 1..K. repeat
1/ EM-rype block pursuit
2: E-step (activation & assignment):
( Rij
3: For each example i, compute current block scores
Gi.k
Ej
Σ Sjespm) log (m))
4: Assign to its best explaining block a; arg max; li.k (ties broken arbitrarily).
5: Estimate per-block activation weights Wi,s = 1fa, = k} and per-feature empirical means
fkjj Wi.a-
6: M-step (feature addition & parameter update):
7: For each block & and each candidate feature & c(m) compute the penalized marginal gain
DRJ = (Fi.d Bk.s - log 2x..) Amx OVERLAP(j, s. s(())
Asp:
where BRg (together with 2R.j) is the one-dimensional maximum-likelinon update for fea- ture under the log-linear model.
8:
Select the best feature j* max, Deg for each block k.
9:
if max; Akj then break
no positive gain remains
11: end if
12: Let k* arg max; A augment s(m+1) +- s(m)) (j*}.
13: Update the corresponding parameters a(m+1) Bkr jr, Zx -. keep other entries
unchanged.
14: Enforce local mutual exclusion and sparsity (Amx: Asp) by pruning conflicting or weak fea-
tures.
15: Re-assignment:
16:
Recompute di.k with updated parameters and reassign examples (lines 4-5).
17: Set m + m + and continue until tolerance/budget is met.
until convergence
18: Return 3 = =3(m), Z = Z(m), and the final structural indicator implied by {5((m) K
Jk=1-
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Comparing image Parsing Template
Largage Photography
Template
Output Photo Guidance
sky
boat
Photography image
Calculate Differences
Output Language Guidance
Image Parsing Tree
Matching 0024
complate
nctivatted duee the incornet position of Scone C.
picture position shown picture.
Comparing Image Parsing Tree with the Template
Explersbbe Pioto and Langaage Photography Gusdanoe
Templato
Output Photo Guidance
sky
Photography
mountain
X
Calculate Differences
Output Language Guidance
Image Parsing Tree
Only the river and sky in
Lack of mountain structure nodes resulting
activated.
Please take the picture according to the compostion shown the picture.
Comparing Parsing Tree with the Template
Explarrbicc Pioto and Langaage Photography Guidanoc
Template
Output Photo Guidance
The setting sun
witti BIR
Photography Image
Suuaide
Calculate Differences
Output Language Guidance
Matching Scors:0.428
Image Parsing
composition contorms
Color semantics nodeo activated scenery.
Suggest taking pictures scene surset environment.
Figure 13: Interpretable evaluation and guidance diagram for photographic images based on com- positional AND-OR templates.
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Movie Poster
Template for Learned Designs
Using Template AIGC Generating
Movie Posters
Cids
Labu Benk
Laty
Eaky Bird
Hark-ollts Fuller
HarryPote
Hall Potter
HaryyPotter Potter Hark Botter
Figure 14: Using the compositional AND-OR template guide the generation of movie posters.
1.0
0.8
0.6
True Positive
0.4
Method (AUC 0.82)
0.2
ResNet (AUC 0.88)
LSVM (AUC 0.77)
HoG+SVM (AUC 0.71)
0.0
0.2
0.4
0.6
0.8
1.0
False Positive Rate
Figure 15: ROC curve scene classification results.
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1.0
0.8
Score from Our Method
0.6
0.2
0.0
0.8
1.0
Score from Subjective Experiment
Figure 16: Results of the user study on template reliability.
Algorithm Recursive
Inference (Parsing and Scoring at Test Time)
Require: Test image I; template TMP with topology, terminal sets, coefficients /3 and normalizers
Z: transformation space T (positions æ, y, scales s, orientations
Ensure: Structural configuration s (activated parts/objects), geometric configuration (loca-
tions/scaless/orinntttionns, and the decomposable consistency score.
1: Terminal responses (MAX1). For each terminal feature j, compute its response map ry (I, for all / T. Optionally allow local jitters u E l and record MAXI LAXI,(0 (1) max ri (I, uot) and the argmax ARGMAXI, (t).
2: Part aggregation (SUM2). For each part & with terminal set ((k) and each T, compute
SUM2+
Σ (EJ(K)
Bk. MAXI,
log Zz. JEJ(M)
Σ
and store backpointers to the maximizing terminals. 3: Object composition (SUM3). For each object hypothesis / T, combine selected parts:
SUM3(t)
Σ 8k SUM2,(t),
k
where Sy E {0, 1} encodes structural choices (OR selections, mutual exclusion). 4: Selection and backtracking, Find 1" arg max, SUM3((). Backtrack through the stored pointers (LMMM--SMM2--AX to obtain the activated parts and their transformations, yielding and g.
5: Output. Return s, g. and the score StM3(t"). The score decomposes additively into object-, relation-, and geometry- contributions under the log-linear model, enabling per-term attri- bution.
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0.875
0.867
0 0.855
0.853
0.850
0.845
0.863
0.855
0.853
0.805
0.724
0.570
0,863
0.861
0.857
0.851
0.345
The Heriirggg
0.864
0.854
0.848
0.845
0.384
0.250
Gave
0.858
0.832
0.833
0.825
0.473
0.361
0.875
0.867
0.855
0.853
lake
0.809
0.794
0.850
0.843
0.831
0.824
0.811
0.805
0.864
0.854
0.838
0.815
0.384
0.250
Figure 17: consistency score based compositional AND-OR templates.
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0.875
0.867
0.855
0.853
3
0.863
0.855
0.853
0.805
0.724
0.870
0.863
0.861
0.857
0.851
0.345
Figure 18: Reliability verification results of compositional AND-OR templates.
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Templates Summarized Photographer
Templates Learned by Our Method
Training Pictures Generated by Midjoumey
Guided by Our Template
Result
Matching Score: 0.856
......
Visual Result
Matching Score: 0.874
000000
Figure 19; Output results based on compositional AND-OR templates.
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STATEMENT ON THE USE OF LARGE LANGUAGE MODELS (LLMs)
Scope. We used an LLM strictly as general-purpose assistant for copy-editing and translation (e.g., abstract, figure captions, and short passages), well minor LaTeX/formattin suggestions (citations, line breaks).
No role in research. The LLM did nol contribute to problem formulation, algorithm model design, implementation, experiments, data or code generation, literature selection, analysis, or con- clusion writing.
Human verification and responsibility. All LLM-suggested edits were reviewed line-by-line by the authors, with factual checks against the original sources avoid unverified claims or copyrighted hird-party text. The authors take full responsibility for the entire content; the LLM is not an author or contributor.
Oineline summary. The LLM was invited as grammar-savyy syanllr-savyypeellchcceee with opinions, not as co-author.
25


Reviews:
[{"review_id": "mJ3lZlMLzG", "reviewer": "Reviewer_drvL", "summary": "The paper proposes a compositional representation for art and design using AND–OR templates (AOTs) under a maximum-entropy log-linear model. The framework defines a unified consistency score (log-likelihood gain vs. a reference distribution) that decomposes into interpretable object-, relation-, and geometry-level evidence. Learning proceeds via a penalized EM-type block-pursuit algorithm with sparsity and local mutual exclusivity constraints, first learning object templates and then reusing them as scene terminals. A semi-supervised structure expansion (SSE) further grows the model from unlabeled images. The method shows interpretability, human alignment, and competitive performance with deep baselines on aesthetic datasets (AVA/AADB) and scene understanding tasks.", "questions": "1. How does the system handle failures or partial detections from YOLO? Does this dependency undermine the claim of a fully interpretable system?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The AND-OR graph structure is inherently interpretable.\n2. The proposed method achieves comparable performance on the AVA dataset, with smaller training parameters.\n3. The proposed SSE is practical and well-justified, bridging labeled and unlabeled data in a structured learning context.", "weaknesses": "1. the expectation module is dependent on YOLO, object that are not part of YOLO detection algorithm are not going to be avilable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NbkYllEg9g", "reviewer": "Reviewer_wZC5", "summary": "This paper proposes a compositional AND-OR template for art and design that includes the part-relation-geometry of images in a structured and interpretable form. It extent AND-OR templates from the object level to the scene level. The learning process is a penalized EM-style block-pursuit with sparsity and local mutual exclusivity. Authors also propose a semi-supervised structural expansion methods which could bootstrap new branches from on the boat high-quality images. Experiments show that the method proposed in this paper achieves good objective consistency and subjective reliability. Compared to other single image important methods, the method in this paper achieves, better consistency score with compositional AND-OR templates.", "questions": "1. The proposed method learn the template based on the training images. Could this message extend to other elements that are not included in the training images?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The AND-OR template for scene proposed in this paper contribute towards more controllable and explainable components for image generation and classification. \n2. Compared to other deep neural network based methods, the master propose in this paper is lightweight with fewer parameters.", "weaknesses": "1. In the objective consistency evaluation, only two examples of the proposed method are presented. The image generated in other ways are not shown for comparison. Yes, we can draw the conclusion that the generated image is mostly consistent to the template, but we can't draw the conclusion about to what extent of the consistency is compared to other way of generation. \n2. In table 1, the accuracy of the proposed method is lower than baseline ViT-B/16 + templates. \n3. In the section 5.1.2 subjective reliability analysis, during the evaluation, the most consistent rate is 43.6%, and the mostly consistent rate is 31.2%, and 12% is inconsistent.  It is unclear how well it is compared to the consistent evolution results based on other methods. And more analysis about the cases labelled as inconsistent are expected.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sKFhG343fy", "reviewer": "Reviewer_jXD7", "summary": "This paper proposes a novel framework for learning compositional, interpretable representations of images, specifically tailored to capture the structural regularities found in art and design. The core idea is a two-level AND-OR Template (AOT). The method is evaluated on curated datasets and standard aesthetic benchmarks (AVA, AADB), showing competitive performance with deep learning baselines while offering superior interpretability and data efficiency.", "questions": "- The templates are learned for specific themes. Can the framework handle or represent more abstract, non-object-centric compositional principles such as \"balance,\" \"rhythm,\" or \"negative space\" that may cut across different concrete themes?\n- The semi-supervised expansion relies on a dual-threshold rule. Could you discuss the sensitivity of the final model to the choice of these thresholds?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed method in this paper offers a transparent, auditable pathway from image features to a qualitative evaluation. The decomposition of the consistency score into object, relation, and geometry terms directly maps to actionable feedback, which is highly valuable for creative assistance tools. The technical approach is sound and thoroughly described. The two-level learning pipeline is a practical way to manage combinatorial complexity. The inclusion of a semi-supervised structural expansion (SSE) mechanism is a thoughtful addition that enhances the framework's scalability and realism.", "weaknesses": "- While the block-pursuit algorithm with sparsity constraints is designed to combat combinatorial explosion, its scalability to highly complex scenes with dozens of interacting objects is not thoroughly demonstrated.\n- The method proposed in this paper focuses exclusively on the structural patterns like relations and geometry. It explicitly does not account for other critical aesthetic factors like lighting, color harmony, texture, and material properties. This limits its comprehensiveness as a full aesthetic assessment tool; it is primarily a compositionalassessment tool.\n- The learning process appears sensitive to the initial seeds. The paper does not fully explore how robust the method is to poor initial templates or how to bootstrap without a high-quality, small labeled set.\n- Some of the images in the paper have low resolutions, caused their details to become blurred. This could be polished in the revised version of this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qWtEuu8xtq", "reviewer": "Reviewer_mmS5", "summary": "This work introduces a compositional AND–OR template framework for art and design. Authors extend classical AND–OR templates (AOTs) from object-level vision to scene-level compositional understanding, and they combine interpretability, compositionally, and aesthetic reasoning within a mathematically rigorous framework.", "questions": "Authors should discuss the detail difference between related work and state their contribution.\n[1] Improving Compositional Generation with Diffusion Models Using Lift Scores\n\nIs the template-based generation method suitable for complex scenes, such as dense crowds and urban street scenes?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The proposed approach produces good interpretability with parse trees and term-level attribution.\n\nAuthors design a maximum-entropy log-linear unified score, and learn via penalized MLE with an EM-type block-pursuit.", "weaknesses": "The method proposed in this paper is too complicated, making it less practical in application.\n\nThe source code provided by the author is confusing, and I find it difficult to see its relevance to this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)