Search Results for author: Kalin Stefanov

Found 16 papers, 5 papers with code

Analysis of Behavior Classification in Motivational Interviewing

no code implementations NAACL (CLPsych) 2021 Leili Tavabi, Trang Tran, Kalin Stefanov, Brian Borsari, Joshua Woolley, Stefan Scherer, Mohammad Soleymani

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.

Classification

AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset

1 code implementation26 Nov 2023 Zhixi Cai, Shreya Ghosh, Aman Pankaj Adatia, Munawar Hayat, Abhinav Dhall, Kalin Stefanov

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.

2k DeepFake Detection +2

ArtHDR-Net: Perceptually Realistic and Accurate HDR Content Creation

no code implementations7 Sep 2023 Hrishav Bakul Barua, Ganesh Krishnasamy, KokSheik Wong, Kalin Stefanov, Abhinav Dhall

High Dynamic Range (HDR) content creation has become an important topic for modern media and entertainment sectors, gaming and Augmented/Virtual Reality industries.

SSIM

S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction

no code implementations13 Jul 2023 Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi, Giampiero Salvi

We address the video prediction task by putting forth a novel model that combines (i) our recently proposed hierarchical residual vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel spatiotemporal PixelCNN (ST-PixelCNN).

Video Prediction

Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization

1 code implementation3 May 2023 Zhixi Cai, Shreya Ghosh, Abhinav Dhall, Tom Gedeon, Kalin Stefanov, Munawar Hayat

The proposed baseline method, Boundary Aware Temporal Forgery Detection (BA-TFD), is a 3D Convolutional Neural Network-based architecture which effectively captures multimodal manipulations.

Binary Classification DeepFake Detection +2

MARLIN: Masked Autoencoder for facial video Representation LearnINg

1 code implementation CVPR 2023 Zhixi Cai, Shreya Ghosh, Kalin Stefanov, Abhinav Dhall, Jianfei Cai, Hamid Rezatofighi, Reza Haffari, Munawar Hayat

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).

Action Classification Attribute +9

Visual Representations of Physiological Signals for Fake Video Detection

no code implementations18 Jul 2022 Kalin Stefanov, Bhawna Paliwal, Abhinav Dhall

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.

Misinformation

Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization

1 code implementation13 Apr 2022 Zhixi Cai, Kalin Stefanov, Abhinav Dhall, Munawar Hayat

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.

Benchmarking DeepFake Detection +1

Webcam-based Eye Gaze Tracking under Natural Head Movement

no code implementations29 Mar 2018 Kalin Stefanov

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

no code implementations24 Nov 2017 Kalin Stefanov, Jonas Beskow, Giampiero Salvi

Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings.

Language Acquisition

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