Paper Reading AI Learner

360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales

2020-09-15 08:45:12
Ashesh Mishra, Hsuan-Tien Lin

Abstract

Gaze estimation involves predicting where the person is looking at, given either a single input image or a sequence of images. One challenging task, gaze estimation in the wild, concerns data collected in unconstrained environments with varying camera-person distances, like the Gaze360 dataset. The varying distances result in varying face sizes in the images, which makes it hard for current CNN backbones to estimate the gaze robustly. Inspired by our natural skill to identify the gaze by taking a focused look at the face area, we propose a novel architecture that similarly zooms in on the face area of the image at multiple scales to improve prediction accuracy. Another challenging task, 360-degree gaze estimation (also introduced by the Gaze360 dataset), consists of estimating not only the forward gazes, but also the backward ones. The backward gazes introduce discontinuity in the yaw angle values of the gaze, making the deep learning models affected by some huge loss around the discontinuous points. We propose to convert the angle values by sine-cosine transform to avoid the discontinuity and represent the physical meaning of the yaw angle better. We conduct ablation studies on both ideas, the novel architecture and the transform, to validate their effectiveness. The two ideas allow our proposed model to achieve state-of-the-art performance for both the Gaze360 dataset and the RT-Gene dataset when using single images. Furthermore, we extend the model to a sequential version that systematically zooms in on a given sequence of images. The sequential version again achieves state-of-the-art performance on the Gaze360 dataset, which further demonstrates the usefulness of our proposed ideas.

Abstract (translated)

URL

https://arxiv.org/abs/2009.06924

PDF

https://arxiv.org/pdf/2009.06924


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot