# MemexQA: Visual Memex Question Answering

arXiv: https://arxiv.org/abs/1708.01336 · HTML: https://mlsanity.com/paper/1708.01336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Navigating Connected Memories with a Task-oriented Dialog System](https://mlsanity.com/paper/2211.08462/) (2022)
- [Life-Bench: A Benchmark and Knowledge Graph Framework for Multimodal Personalization Beyond Concept Recognition](https://mlsanity.com/paper/2602.19001/) (2026)
- [PAL-Bench: Evidence-Grounded Profile Reconstruction from Longitudinal Personal Albums](https://mlsanity.com/paper/2606.16175/) (2026)

## Abstract (arXiv)

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection. Towards solving the task, we 1) present the MemexQA dataset, a large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network architecture for image, text and video question answering. Experimental results on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines and yields the state-of-the-art on this novel and challenging task. The promising results on TextQA and VideoQA suggest MemexNet's efficacy and scalability across various QA tasks.