# Hand-Priming in Object Localization for Assistive Egocentric Vision

arXiv: https://arxiv.org/abs/2002.12557 · HTML: https://mlsanity.com/paper/2002.12557/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation](https://mlsanity.com/paper/2107.02718/) (2021)
- [ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction](https://mlsanity.com/paper/2504.01472/) (2025)
- [GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery](https://mlsanity.com/paper/2101.07314/) (2021)
- [Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired](https://mlsanity.com/paper/2508.03651/) (2025)
- [Multimodal Knowledge Distillation for Egocentric Action Recognition Robust to Missing Modalities](https://mlsanity.com/paper/2504.08578/) (2025)
- [A Large Vision-Language Model based Environment Perception System for Visually Impaired People](https://mlsanity.com/paper/2504.18027/) (2025)
- [PartHOI: Part-based Hand-Object Interaction Transfer via Generalized Cylinders](https://mlsanity.com/paper/2504.20599/) (2025)

## Abstract (arXiv)

Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population. While we strive to improve recognition performance, it remains difficult to identify which object is of interest to the user; the object may not even be included in the frame due to challenges in camera aiming without visual feedback. Also, gaze information, commonly used to infer the area of interest in egocentric vision, is often not dependable. However, blind users often tend to include their hand either interacting with the object that they wish to recognize or simply placing it in proximity for better camera aiming. We propose localization models that leverage the presence of the hand as the contextual information for priming the center area of the object of interest. In our approach, hand segmentation is fed to either the entire localization network or its last convolutional layers. Using egocentric datasets from sighted and blind individuals, we show that the hand-priming achieves higher precision than other approaches, such as fine-tuning, multi-class, and multi-task learning, which also encode hand-object interactions in localization.