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Found 6 papers, 0 papers with code
Learning User Embeddings from Human Gaze for Personalised Saliency Prediction
At the core of our method is a Siamese convolutional neural encoder that learns the user embeddings by contrasting the image and personal saliency map pairs of different users.
SeFFeC: Semantic Facial Feature Control for Fine-grained Face Editing
To ensure that the desired feature measurement is changed towards the target value without altering uncorrelated features, we introduced a novel semantic face feature loss.
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning
We show that intentions of human players, i. e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma's Revenge - one of the most challenging RL tasks in the Atari2600 game suite.
VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answering
We present VQA-MHUG - a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker.
Neural Photofit: Gaze-based Mental Image Reconstruction
The encoder extracts image features and predicts a neural activation map for each face looked at by a human observer.
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification.
