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Found 17 papers, 6 papers with code
Analysis of Behavior Classification in Motivational Interviewing
Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client’s behavioral outcome.
1M-Deepfakes Detection Challenge
The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security.
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
High Dynamic Range (HDR) content (i. e., images and videos) has a broad range of applications.
Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence
In this paper we study the variations in human brain activity when listening to real and fake audio.
HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes.
AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
The comprehensive benchmark of the proposed dataset utilizing state-of-the-art deepfake detection and localization methods indicates a significant drop in performance compared to previous datasets.
ArtHDR-Net: Perceptually Realistic and Accurate HDR Content Creation
High Dynamic Range (HDR) content creation has become an important topic for modern media and entertainment sectors, gaming and Augmented/Virtual Reality industries.
S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction
We address the video prediction task by putting forth a novel model that combines (i) a novel hierarchical residual learning vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel autoregressive spatiotemporal predictive model (AST-PM).
Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization
The proposed baseline method, Boundary Aware Temporal Forgery Detection (BA-TFD), is a 3D Convolutional Neural Network-based architecture which effectively captures multimodal manipulations.
Ranked #1 on
Temporal Forgery Localization
on ForgeryNet
MARLIN: Masked Autoencoder for facial video Representation LearnINg
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS).
Ranked #1 on
Emotion Classification
on CMU-MOSEI
Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation
We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data.
Visual Representations of Physiological Signals for Fake Video Detection
We investigate two strategies for combining the video and physiology modalities, either by augmenting the video with information from the physiology or by novelly learning the fusion of those two modalities with a proposed Graph Convolutional Network architecture.
Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization
Our baseline method for benchmarking the proposed dataset is a 3DCNN model, termed as Boundary Aware Temporal Forgery Detection (BA-TFD), which is guided via contrastive, boundary matching, and frame classification loss functions.
Ranked #1 on
DeepFake Detection
on LAV-DF
Webcam-based Eye Gaze Tracking under Natural Head Movement
Furthermore, we can report that the proposed tracker commits a mean error of (87. 18, 103. 86) pixels in x and y direction, respectively, under natural head movement.
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition
Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings.
A Multi-party Multi-modal Dataset for Focus of Visual Attention in Human-human and Human-robot Interaction
This papers describes a data collection setup and a newly recorded dataset.
The Tutorbot Corpus --- A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue
The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions.
