Semantically Stable Image Composition Analysisvia Saliency and Gradient Vector Flow Fusion

• URL: http://arxiv.org/abs/2604.16500v1
• Date: Tue, 14 Apr 2026 09:53:19 GMT
• Title: Semantically Stable Image Composition Analysisvia Saliency and Gradient Vector Flow Fusion
• Abstract summary: We introduce VFCNet, which fuses saliency and edge information into a gradient vector flow field.<n>The model computes dual-stream GVF representations, integrates them via attention, and extracts multi-scale flow features.<n>VFCNet achieves state-of-the-art performance on the PICD benchmark.
• Score: 0.5249805590164902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliable computational assessment of photographic composition requires features that are discriminative of spatial layout yet robust to semantic content. This paper proposes a low-level representation grounded in the assumption that composition can be understood as the flow of visual attention across geometric structure. We introduce VFCNet, which fuses saliency and edge information into a gradient vector flow (GVF) field. The model computes dual-stream GVF representations, integrates them via attention, and extracts multi-scale flow features with a DINOv3 backbone. VFCNet achieves state-of-the-art performance on the PICD benchmark (CDA-1: 0.683, CDA-2: 0.629), improving by 33.1\% and 36.1\% over the previous best method. We also show that a simple classifier on self-supervised DINOv3 features substantially outperforms more sophisticated, composition-specialized models. Code is available at https://github.com/ADadras/VFCNet

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