# GLANCE: Gaze-Led Attention Network for Compressed Edge-inference

arXiv: https://arxiv.org/abs/2603.15717 · HTML: https://mlsanity.com/paper/2603.15717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge](https://mlsanity.com/paper/1904.04421/) (2019)
- [Appearance-Based Gaze Estimation Using Dilated-Convolutions](https://mlsanity.com/paper/1903.07296/) (2019)
- [Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings](https://mlsanity.com/paper/1805.04771/) (2018)
- [Differentiable Weightless Neural Networks](https://mlsanity.com/paper/2410.11112/) (2024)
- [Weightless Neural Networks for Efficient Edge Inference](https://mlsanity.com/paper/2203.01479/) (2022)
- [HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI](https://mlsanity.com/paper/2408.03956/) (2024)

## Abstract (arXiv)

Real-time object detection in AR/VR systems faces critical computational constraints, requiring sub-10,ms latency within tight power budgets. Inspired by biological foveal vision, we propose a two-stage pipeline that combines differentiable weightless neural networks for ultra-efficient gaze estimation with attention-guided region-of-interest object detection. Our approach eliminates arithmetic-intensive operations by performing gaze tracking through memory lookups rather than multiply-accumulate computations, achieving an angular error of 8.32circ with only 393 MACs and 2.2 KiB of memory per frame. Gaze predictions guide selective object detection on attended regions, reducing computational burden by 40-50% and energy consumption by 65%. Deployed on the Arduino Nano 33 BLE, our system achieves 48.1% mAP on COCO (51.8% on attended objects) while maintaining sub-10,ms latency, meeting stringent AR/VR requirements by improving the communication time by × 177. Compared to the global YOLOv12n baseline, which achieves 39.2%, 63.4%, and 83.1% accuracy for small, MEDium, and LARGE objects, respectively, the ROI-based method yields 51.3%, 72.1%, and 88.1% under the same settings. This work shows that memory-centric architectures with explicit attention modeling offer better efficiency and accuracy for resource-constrained wearable platforms than uniform processing.