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Found 22 papers, 2 papers with code
The CASTLE 2024 Dataset: Advancing the Art of Multimodal Understanding
Egocentric video has seen increased interest in recent years, as it is used in a range of areas.
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior
This paper addresses key challenges in object-centric representation learning of video.
Memories in the Making: Predicting Video Memorability with Encoding Phase EEG
In a world of ephemeral moments, our brain diligently sieves through a cascade of experiences, like a skilled gold prospector searching for precious nuggets amidst the river's relentless flow.
Diffusing Surrogate Dreams of Video Scenes to Predict Video Memorability
As part of the MediaEval 2022 Predicting Video Memorability task we explore the relationship between visual memorability, the visual representation that characterises it, and the underlying concept portrayed by that visual representation.
Overview of The MediaEval 2022 Predicting Video Memorability Task
This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022.
Experiences from the MediaEval Predicting Media Memorability Task
The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time.
An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices
Using our proposed model architecture, we compare the accuracy between stress detection models that use measures from each individual signal source, and one model employing the fusion of multiple sensor sources.
Predicting Media Memorability: Comparing Visual, Textual and Auditory Features
This paper describes our approach to the Predicting Media Memorability task in MediaEval 2021, which aims to address the question of media memorability by setting the task of automatically predicting video memorability.
Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability
The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals -- either alone or in combination with other data sources -- in the context of predicting video memorability by highlighting the utility of EEG data.
Overview of The MediaEval 2021 Predicting Media Memorability Task
This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task.
An Annotated Video Dataset for Computing Video Memorability
Data includes the reaction times for each recognition of each video.
The Influence of Audio on Video Memorability with an Audio Gestalt Regulated Video Memorability System
We introduce a novel multimodal deep learning-based late-fusion system that uses audio gestalt to estimate the influence of a given video's audio on its overall short-term recognition memorability, and selectively leverages audio features to make a prediction accordingly.
Leveraging Audio Gestalt to Predict Media Memorability
Memorability determines what evanesces into emptiness, and what worms its way into the deepest furrows of our minds.
Investigating Memorability of Dynamic Media
The Predicting Media Memorability task in MediaEval'20 has some challenging aspects compared to previous years.
Contrastive Representation Learning: A Framework and Review
Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented.
A Neuro-AI Interface for Evaluating Generative Adversarial Networks
In this work, we introduce an evaluation metric called Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.
A Neuro-AI Interface: Learning DNNs from the Human Brain
Deep neural networks (DNNs) are inspired from the human brain and the interconnection between the two has been widely studied in the literature.
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks
In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals.
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR).
Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation
We propose a novel approach that combines a brain-computer interface (BCI) with GANs to generate a measure we call Neuroscore, which closely mirrors the behavioral ground truth measured from participants tasked with discerning real from synthetic images.
Exploring EEG for Object Detection and Retrieval
We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.
