Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation

• URL: http://arxiv.org/abs/2409.00912v1
• Date: Mon, 2 Sep 2024 02:51:40 GMT
• Title: Merging Multiple Datasets for Improved Appearance-Based Gaze Estimation
• Authors: Liang Wu, Bertram E. Shi,
• Abstract summary: Two-stage Transformer-based Gaze-feature Fusion (TTGF) method uses transformers to merge information from each eye and the face separately and then merge across the two eyes. Our proposed Gaze Adaptation Module (GAM) method handles annotation inconsis-tency by applying a Gaze Adaption Module for each dataset to correct gaze estimates from a single shared estimator.
• Score: 10.682719521609743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multiple datasets have been created for training and testing appearance-based gaze estimators. Intuitively, more data should lead to better performance. However, combining datasets to train a single esti-mator rarely improves gaze estimation performance. One reason may be differences in the experimental protocols used to obtain the gaze sam-ples, resulting in differences in the distributions of head poses, gaze an-gles, illumination, etc. Another reason may be the inconsistency between methods used to define gaze angles (label mismatch). We propose two innovations to improve the performance of gaze estimation by leveraging multiple datasets, a change in the estimator architecture and the intro-duction of a gaze adaptation module. Most state-of-the-art estimators merge information extracted from images of the two eyes and the entire face either in parallel or combine information from the eyes first then with the face. Our proposed Two-stage Transformer-based Gaze-feature Fusion (TTGF) method uses transformers to merge information from each eye and the face separately and then merge across the two eyes. We argue that this improves head pose invariance since changes in head pose affect left and right eye images in different ways. Our proposed Gaze Adaptation Module (GAM) method handles annotation inconsis-tency by applying a Gaze Adaption Module for each dataset to correct gaze estimates from a single shared estimator. This enables us to combine information across datasets despite differences in labeling. Our experi-ments show that these innovations improve gaze estimation performance over the SOTA both individually and collectively (by 10% - 20%). Our code is available at https://github.com/HKUST-NISL/GazeSetMerge.

Related papers

• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
  We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
  arXiv  Detail & Related papers  (2023-08-17T14:14:28Z)
• Towards Self-Supervised Gaze Estimation [32.91601919228028]
  We propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks.
  arXiv  Detail & Related papers  (2022-03-21T13:35:16Z)
• 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)
• The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation [24.833385815585405]
  We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences. Specifically, we first assume that we can obtain some initial gaze prediction results with existing method, which we refer to as InitNet. We validate our ideas on three publicly available datasets, EVE, XGaze and MPIIGaze and demonstrate that our proposed method outperforms the SOTA methods significantly.
  arXiv  Detail & Related papers  (2021-06-27T10:14:10Z)
• Adaptive Feature Fusion Network for Gaze Tracking in Mobile Tablets [19.739595664816164]
  We propose a novel Adaptive Feature Fusion Network (AFF-Net), which performs gaze tracking task in mobile tablets. We use Squeeze-and-Excitation layers to adaptively fuse two-eye features according to their similarity on appearance. Experiments on both GazeCapture and MPIIFaceGaze datasets demonstrate consistently superior performance of the proposed method.
  arXiv  Detail & Related papers  (2021-03-20T07:16:10Z)
• DecAug: Augmenting HOI Detection via Decomposition [54.65572599920679]
  Current algorithms suffer from insufficient training samples and category imbalance within datasets. We propose an efficient and effective data augmentation method called DecAug for HOI detection. Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements on V-COCO and HICODET dataset.
  arXiv  Detail & Related papers  (2020-10-02T13:59:05Z)
• 360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales [26.36068336169795]
  We develop a model that mimics humans' ability to estimate the gaze by aggregating from focused looks. The model avoids the need to extract clear eye patches. We extend the model to handle the challenging task of 360-degree gaze estimation.
  arXiv  Detail & Related papers  (2020-09-15T08:45:12Z)
• Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild [82.42401132933462]
  We present a solution that works without the need for precise annotations of the gaze angle and the head pose. Our method consists of three novel modules: the Gaze Correction module (GCM), the Gaze Animation module (GAM), and the Pretrained Autoencoder module (PAM)
  arXiv  Detail & Related papers  (2020-08-09T23:14:16Z)
• Towards End-to-end Video-based Eye-Tracking [50.0630362419371]
  Estimating eye-gaze from images alone is a challenging task due to un-observable person-specific factors. We propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships. We demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures.
  arXiv  Detail & Related papers  (2020-07-26T12:39:15Z)
• Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation [90.28365183660438]
  This paper proposes an augmented parallel-pyramid net with attention partial module and differentiable auto-data augmentation. We define a new pose search space where the sequences of data augmentations are formulated as a trainable and operational CNN component. Notably, our method achieves the top-1 accuracy on the challenging COCO keypoint benchmark and the state-of-the-art results on the MPII datasets.
  arXiv  Detail & Related papers  (2020-03-17T03:52:17Z)

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.