# GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery

arXiv: https://arxiv.org/abs/2101.07314 · HTML: https://mlsanity.com/paper/2101.07314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Understanding Human Hands in Contact at Internet Scale](https://mlsanity.com/paper/2006.06669/) (2020)
- [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://mlsanity.com/paper/1804.09627/) (2018)
- [Unsupervised Learning of Important Objects from First-Person Videos](https://mlsanity.com/paper/1611.05335/) (2016)
- [Hand-Priming in Object Localization for Assistive Egocentric Vision](https://mlsanity.com/paper/2002.12557/) (2020)
- [Ego-Object Discovery](https://mlsanity.com/paper/1504.01639/) (2015)
- [Where is my Phone ? Personal Object Retrieval from Egocentric Images](https://mlsanity.com/paper/1608.08139/) (2016)

## Built on by (most cited first, 2 of 2)

- [AMEGO: Active Memory from long EGOcentric videos](https://mlsanity.com/paper/2409.10917/) (2024)
- [Background Mixup Data Augmentation for Hand and Object-in-Contact Detection](https://mlsanity.com/paper/2202.13941/) (2022)

## Abstract (arXiv)

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide information on their locations have been developed. However, prior systems for assisting people in finding objects require users to register the target objects in advance. This requirement imposes a cumbersome burden on the users, and the system cannot help remind them of unexpectedly lost objects. We propose GO-Finder ("Generic Object Finder"), a registration-free wearable camera based system for assisting people in finding an arbitrary number of objects based on two key features: automatic discovery of hand-held objects and image-based candidate selection. Given a video taken from a wearable camera, Go-Finder automatically detects and groups hand-held objects to form a visual timeline of the objects. Users can retrieve the last appearance of the object by browsing the timeline through a smartphone app. We conducted a user study to investigate how users benefit from using GO-Finder and confirmed improved accuracy and reduced mental load regarding the object search task by providing clear visual cues on object locations.