One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with
Single Encoders
- URL: http://arxiv.org/abs/2211.11936v1
- Date: Tue, 22 Nov 2022 01:12:31 GMT
- Title: One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with
Single Encoders
- Authors: Rishi Athavale, Lakshmi Sritan Motati, Rohan Kalahasty
- Abstract summary: We propose a gaze estimation model that implements the ResNet and Inception model architectures and makes predictions using only one eye image.
With the use of lightweight architectures, we achieve high performance on the GazeCapture dataset with very low model parameter counts.
We also notice significantly lower errors on the right eye images in the test set, which could be important in the design of future gaze estimation-based tools.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Gaze estimation has grown rapidly in accuracy in recent years. However, these
models often fail to take advantage of different computer vision (CV)
algorithms and techniques (such as small ResNet and Inception networks and
ensemble models) that have been shown to improve results for other CV problems.
Additionally, most current gaze estimation models require the use of either
both eyes or an entire face, whereas real-world data may not always have both
eyes in high resolution. Thus, we propose a gaze estimation model that
implements the ResNet and Inception model architectures and makes predictions
using only one eye image. Furthermore, we propose an ensemble calibration
network that uses the predictions from several individual architectures for
subject-specific predictions. With the use of lightweight architectures, we
achieve high performance on the GazeCapture dataset with very low model
parameter counts. When using two eyes as input, we achieve a prediction error
of 1.591 cm on the test set without calibration and 1.439 cm with an ensemble
calibration model. With just one eye as input, we still achieve an average
prediction error of 2.312 cm on the test set without calibration and 1.951 cm
with an ensemble calibration model. We also notice significantly lower errors
on the right eye images in the test set, which could be important in the design
of future gaze estimation-based tools.
Related papers
- Establishing a Baseline for Gaze-driven Authentication Performance in VR: A Breadth-First Investigation on a Very Large Dataset [10.645578300818498]
This paper establishes a baseline for gaze-driven authentication performance using a very large dataset of gaze recordings from 9202 people.
Our major findings indicate that gaze authentication can be as accurate as required by the FIDO standard when driven by a state-of-the-art machine learning architecture and a sufficiently large training dataset.
arXiv Detail & Related papers (2024-04-17T23:33:34Z) - Automated Classification of Model Errors on ImageNet [7.455546102930913]
We propose an automated error classification framework to study how modeling choices affect error distributions.
We use our framework to comprehensively evaluate the error distribution of over 900 models.
In particular, we observe that the portion of severe errors drops significantly with top-1 accuracy indicating that, while it underreports a model's true performance, it remains a valuable performance metric.
arXiv Detail & Related papers (2023-11-13T20:41:39Z) - Proximity-Informed Calibration for Deep Neural Networks [49.330703634912915]
ProCal is a plug-and-play algorithm with a theoretical guarantee to adjust sample confidence based on proximity.
We show that ProCal is effective in addressing proximity bias and improving calibration on balanced, long-tail, and distribution-shift settings.
arXiv Detail & Related papers (2023-06-07T16:40:51Z) - Bridging Precision and Confidence: A Train-Time Loss for Calibrating
Object Detection [58.789823426981044]
We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions.
Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
arXiv Detail & Related papers (2023-03-25T08:56:21Z) - Sample-dependent Adaptive Temperature Scaling for Improved Calibration [95.7477042886242]
Post-hoc approach to compensate for neural networks being wrong is to perform temperature scaling.
We propose to predict a different temperature value for each input, allowing us to adjust the mismatch between confidence and accuracy.
We test our method on the ResNet50 and WideResNet28-10 architectures using the CIFAR10/100 and Tiny-ImageNet datasets.
arXiv Detail & Related papers (2022-07-13T14:13:49Z) - Core Risk Minimization using Salient ImageNet [53.616101711801484]
We introduce the Salient Imagenet dataset with more than 1 million soft masks localizing core and spurious features for all 1000 Imagenet classes.
Using this dataset, we first evaluate the reliance of several Imagenet pretrained models (42 total) on spurious features.
Next, we introduce a new learning paradigm called Core Risk Minimization (CoRM) whose objective ensures that the model predicts a class using its core features.
arXiv Detail & Related papers (2022-03-28T01:53:34Z) - L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments [2.5234156040689237]
We propose a robust CNN-based model for predicting gaze in unconstrained settings.
We use two identical losses, one for each angle, to improve network learning and increase its generalization.
Our proposed model achieves state-of-the-art accuracy of 3.92deg and 10.41deg on MPIIGaze and Gaze360 datasets, respectively.
arXiv Detail & Related papers (2022-03-07T12:35:39Z) - When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model [87.25037167380522]
We propose a model that is accurate, robust, efficient, generalizable, and end-to-end trainable.
In order to achieve a better accuracy, we propose two lightweight modules.
DQInit dynamically initializes the queries of decoder from the inputs, enabling the model to achieve as good accuracy as the ones with multiple decoder layers.
QAMem is designed to enhance the discriminative ability of queries on low-resolution feature maps by assigning separate memory values to each query rather than a shared one.
arXiv Detail & Related papers (2021-05-27T13:51:42Z) - Multiple Run Ensemble Learning withLow-Dimensional Knowledge Graph
Embeddings [4.317340121054659]
We propose a simple but effective performance boosting strategy for knowledge graph embedding (KGE) models.
We repeat the training of a model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing.
We show that our approach enables different models to better cope with their issues on modeling various graph patterns.
arXiv Detail & Related papers (2021-04-11T12:26:50Z) - Calibrating Deep Neural Networks using Focal Loss [77.92765139898906]
Miscalibration is a mismatch between a model's confidence and its correctness.
We show that focal loss allows us to learn models that are already very well calibrated.
We show that our approach achieves state-of-the-art calibration without compromising on accuracy in almost all cases.
arXiv Detail & Related papers (2020-02-21T17:35:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.