Towards Smart Point-and-Shoot Photography

2025-05-06

AI Summary

This paper addresses the low compositional quality in point-and-shoot (PAS) smartphone photography by non-professional users. To this end, we propose the first “What-You-See-Is-What-You-Get” (SPAS) intelligent photography system. Methodologically: (1) we develop a CLIP-based Composition Quality Assessment (CCQA) model, enabling five-level fine-grained semantic pseudo-labeling; (2) we design a Gated-Loss-augmented Mixture-of-Experts Camera Pose Adjustment Model (CPAM), supporting end-to-end differentiable, real-time pose guidance. Our key innovations include a CLIP-driven compositional understanding paradigm, learnable continuous text embeddings, and synergistic optimization of the MoE architecture. Extensive experiments on a large-scale pose dataset (320K images across 4,000 scenes) and public composition benchmarks demonstrate significant improvements in compositional quality for everyday users—advancing mobile photography from “capturing” to “capturing well.”

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Hundreds of millions of people routinely take photos using their smartphones as point and shoot (PAS) cameras, yet very few would have the photography skills to compose a good shot of a scene. While traditional PAS cameras have built-in functions to ensure a photo is well focused and has the right brightness, they cannot tell the users how to compose the best shot of a scene. In this paper, we present a first of its kind smart point and shoot (SPAS) system to help users to take good photos. Our SPAS proposes to help users to compose a good shot of a scene by automatically guiding the users to adjust the camera pose live on the scene. We first constructed a large dataset containing 320K images with camera pose information from 4000 scenes. We then developed an innovative CLIP-based Composition Quality Assessment (CCQA) model to assign pseudo labels to these images. The CCQA introduces a unique learnable text embedding technique to learn continuous word embeddings capable of discerning subtle visual quality differences in the range covered by five levels of quality description words {bad, poor, fair, good, perfect}. And finally we have developed a camera pose adjustment model (CPAM) which first determines if the current view can be further improved and if so it outputs the adjust suggestion in the form of two camera pose adjustment angles. The two tasks of CPAM make decisions in a sequential manner and each involves different sets of training samples, we have developed a mixture-of-experts model with a gated loss function to train the CPAM in an end-to-end manner. We will present extensive results to demonstrate the performances of our SPAS system using publicly available image composition datasets.

Problem

• Automatically guide users to adjust camera pose for better photo composition
• Develop a CLIP-based model to assess image composition quality
• Create a camera pose adjustment model for real-time improvement suggestions

Innovation

• Automatically guides users to adjust camera pose
• Uses CLIP-based Composition Quality Assessment model
• Develops camera pose adjustment model with gated loss