Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthetic judgment remain entangled. We propose SE-MoLoRA, a modular parameter-efficient adaptation framework for domain-specific photographic assessment. The method separates general photographic knowledge from specialist residual judgments using an always-active shared LoRA expert and routed adapters for composition, lighting, and technical quality. A lightweight query router selects the relevant specialist, enabling targeted critique without training separate full models. A rank-64 shared adapter captures broad photographic vocabulary, while rank-32 specialists learn domain-specific residuals with an orthogonal regularization penalty that encourages disentangled representations. Training data is obtained by distilling the Reddit Photo Critique Dataset into domain-labeled critique samples. On held-out critique generation, SE-MoLoRA improves BERTScore-F1 from 0.2317 to 0.4215 over monolithic LoRA and is preferred in 84.6% of pairwise comparisons, while using fewer active parameters than separate specialist models. SVD-based ablation study shows that shared-specialist decomposition and orthogonal regularization reduce expert overlap. These results demonstrate that modular adaptation improves controllability and specificity in multimodal photographic critique.
Figures & tables
Figure 1: The SE-MoLoRA architecture. A shared adapter handles general photographic knowledge, while specialized adapters for composition, lighting, and technical aspects are conditionally activated based on the router’s decision.
Expert
Architecture
BERTScore F1 ( ↑ )
SBERT (Cos Sim) ( ↑ )
ROUGE-L F1 ( ↑ )
Shared
Base Model
0.0379 ± 0.0751
0.6105 ± 0.1140
0.0921 ± 0.0309
Monolithic
0.2317 ± 0.0831
0.6337 ± 0.1013
0.1787 ± 0.0629
SE-MoLoRA
0.4215 ± 0.1000
0.6851 ± 0.1061
0.3339 ± 0.1018
Composition
Base Model
-0.0211 ± 0.0478
0.5277 ± 0.1232
0.0810 ± 0.0270
Monolithic
0.2348 ± 0.0773
0.5362 ± 0.1265
0.1845 ± 0.0530
SE-MoLoRA
0.2999 ± 0.0745
0.5851 ± 0.1336
0.2145 ± 0.0573
Table 1: Automated metric evaluation comparing the zero-shot Base model, a Monolithic LoRA baseline, and our routed SE-MoLoRA architecture. Best results are highlighted in bold. A paired Student’s t-test confirmed statistical significance ( p<0.001 ).
Figure 2: LLM-as-a-Judge Evaluation Win Rates of the SE-MoLoRA architecture against the zero-shot base model (Top) and the monolithic adapter (Bottom).
Figure 3: Qualitative comparison between responses from each expert for the same image (Sample Taken from RPCD Nieto et al. (2022) ). Domain-specific vocabulary for each expert is highlighted in purple.
Figure 4: SVD-based expert disentanglement across different λortho constraints.
Figure 5: Confusion matrix for the text-only and multimodal router.
Benchmark
Text Router
Multimodal Router
Q-Bench
55.51%
57.65%
PhotoBench
50.66%
53.97%
Table 3: Router comparison on zero-shot aesthetic benchmarks.
Figure 6: Ablation results showing the impact of the shared expert on win rates with LLM-as-judge.
Model
Accuracy (%)
Random guess ∗
37.80
GPT-4o (2024-08-06) ∗
73.04
AesExpert ∗ Huang et al. (2024b)
64.15
Q-Instruct ∗ Wu et al. (2024)
67.09
PhotoEye ∗ Qi et al. (2025)
74.50
Qwen3-VL-8B
50.79
Table 4: Comparison of SE-MoLoRA with State-of-the-Art image aesthetic methods on Q-Bench (left) and PhotoBench (right). Results indicated by ∗ were reported in Qi et al. (2025) .
Figure 7: Qualitative comparison of SE-MoLoRA and base model on adversarial images (Sample Taken from RPCD Nieto et al. (2022) ).
Vision-language model adaptation tailors pretrained image-text and multimodal models to new tasks and domains while retaining transferable capabilities.