How to Take a Memorable Picture? Empowering Users with Actionable Feedback

TL;DR

The results indicate that memorability can not only be predicted but also taught and instructed, shifting the focus from mere prediction to actionable feedback for human creators.

Abstract

Image memorability, i.e., how likely an image is to be remembered, has traditionally been studied in computer vision either as a passive prediction task, with models regressing a scalar score, or with generative methods altering the visual input to boost the image likelihood of being remembered. Yet, none of these paradigms supports users at capture time, when the crucial question is how to improve a photo memorability. We introduce the task of Memorability Feedback (MemFeed), where an automated model should provide actionable, human-interpretable guidance to users with the goal to enhance an image future recall. We also present MemCoach, the first approach designed to provide concrete suggestions in natural language for memorability improvement (e.g., "emphasize facial expression," "bring the subject forward"). Our method, based on Multimodal Large Language Models (MLLMs), is training-free and employs a teacher-student steering strategy, aligning the model internal activations toward more memorable patterns learned from a teacher model progressing along least-to-most memorable samples. To enable systematic evaluation on this novel task, we further introduce MemBench, a new benchmark featuring sequence-aligned photoshoots with annotated memorability scores. Our experiments, considering multiple MLLMs, demonstrate the effectiveness of MemCoach, showing consistently improved performance over several zero-shot models. The results indicate that memorability can not only be predicted but also taught and instructed, shifting the focus from mere prediction to actionable feedback for human creators.

Figures (7)

• Figure 1: Given an input photo, memorability feedback aims to generate natural-language suggestions to guide users toward a more memorable shot. MemCoach provides memorability-aware feedback, effectively assisting users to capture memorable images.
• Figure 2: Overview of MemBench generation and evaluation. Top: Data pipeline for constructing MemBench, including scene grouping, memorability regression, image ranking, and generation of actionable memorability-aware feedback. Bottom: Evaluation pipeline assessing feedback quality through editing-based memorability improvement and perplexity scoring.
• Figure 3: MemBench statistics. Data analysis in terms of (a) most frequent words; (b) distribution of memorability scores for the least and most memorable images within each scene; (c) feedback length as measured by content words; and (d) categorization of atomic sub-actions, where the width of each chord indicates the frequency of co-occurrence between categories.
• Figure 5: Overview of the proposed method.(a) Contrasting data generation: paired samples are built by coupling the memorability-aware guidance of a teacher MLLM with the neutral responses of a student MLLM on the same scene; (b) Steering vector extraction: activation differences between memorability-aware and neutral feedback are averaged to obtain a memorability steering vector241, 50, 4285, 194, 248 capturing the latent shift toward effective suggestions for memorability; (c) Inference with MLLM steering: the student activations are shifted using the memorability steering vector to produce improved, memorability-oriented feedback without additional training.
• Figure 6: Common feedback patterns on source images. MemCoach favors symmetric and socially connected compositions, reflecting principles of human photography.