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Scanpath prediction
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Learning to Predict Sequences of Human Fixations.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision
GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths
Look Hear: Gaze Prediction for Speech-directed Human Attention
OAT: Object-Level Attention Transformer for Gaze Scanpath Prediction
Pathformer3D: A 3D Scanpath Transformer for 360° Images
Predicting Visual Attention in Graphic Design Documents
Multimodal Learning and Cognitive Processes in Radiology: MedGaze for Chest X-ray Scanpath Prediction
Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models
Beyond Average: Individualized Visual Scanpath Prediction
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images
Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers
An Inter-observer consistent deep adversarial training for visual scanpath prediction
A domain adaptive deep learning solution for scanpath prediction of paintings
A Probabilistic Time-Evolving Approach to Scanpath Prediction
SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images
A Simple and efficient deep Scanpath Prediction
Scanpath Prediction on Information Visualisations
SALYPATH: A Deep-Based Architecture for visual attention prediction
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
State-of-the-Art in Human Scanpath Prediction
On gaze deployment to audio-visual cues of social interactions
Wave Propagation of Visual Stimuli in Focus of Attention
Gravitational Laws of Focus of Attention
PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks
SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.