19 citations · 36 across the 18 of their papers we have counts for
19 papers
SignMAE: Segmentation-Driven Self-Supervised Learning for Sign Language Recognition
Kunyuan Xie, Zhixi Cai, Kalin Stefanov
Subtle hand differences make sign language recognition challenging, yet many existing methods rely on encoders pretrained on generic action datasets that poorly capture such fine-g…
DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors
Kaustubh Kundu, Hrishav Bakul Barua, Lucy Robertson-Bell +2
The trend in sign language generation is centered around data-driven generative methods that require vast amounts of precise 2D and 3D human pose data to achieve an acceptable gene…
Do Blind Spots Matter for Word-Referent Mapping? A Computational Study with Infant Egocentric Video
Zekai Shi, Zhixi Cai, Kalin Stefanov
Typically, children start to learn their first words between 6 and 9 months, linking spoken utterances to their visual referents. Without prior knowledge, a word encountered for th…
PhysHDR: When Lighting Meets Materials and Scene Geometry in HDR Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Ganesh Krishnasamy +2
Low Dynamic Range (LDR) to High Dynamic Range (HDR) image translation is a fundamental task in many computational vision problems. Numerous data-driven methods have been proposed t…
A Cycle Ride to HDR: Semantics Aware Self-Supervised Framework for Unpaired LDR-to-HDR Image Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, Lemuel Lai En Che +3
Reconstruction of High Dynamic Range (HDR) from Low Dynamic Range (LDR) images is an important computer vision task. There is a significant amount of research utilizing both conven…
1M-Deepfakes Detection Challenge
Zhixi Cai, Abhinav Dhall, Shreya Ghosh +4
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 dig…
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
Hrishav Bakul Barua, Kalin Stefanov, KokSheik Wong +2
High Dynamic Range (HDR) content (i.e., images and videos) has a broad range of applications. However, capturing HDR content from real-world scenes is expensive and time-consuming.…
HistoHDR-Net: Histogram Equalization for Single LDR to HDR Image Translation
Hrishav Bakul Barua, Ganesh Krishnasamy, KokSheik Wong +2
High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offer…
Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence
Mahsa Salehi, Kalin Stefanov, Ehsan Shareghi
In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-…
AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
Zhixi Cai, Shreya Ghosh, Aman Pankaj Adatia +4
The detection and localization of highly realistic deepfake audio-visual content are challenging even for the most advanced state-of-the-art methods. While most of the research eff…
ArtHDR-Net: Perceptually Realistic and Accurate HDR Content Creation
Hrishav Bakul Barua, Ganesh Krishnasamy, KokSheik Wong +2
High Dynamic Range (HDR) content creation has become an important topic for modern media and entertainment sectors, gaming and Augmented/Virtual Reality industries. Many methods ha…
S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction
Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi +1
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), an…
Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization
Zhixi Cai, Shreya Ghosh, Abhinav Dhall +3
Most deepfake detection methods focus on detecting spatial and/or spatio-temporal changes in facial attributes and are centered around the binary classification task of detecting w…
MARLIN: Masked Autoencoder for facial video Representation LearnINg
Zhixi Cai, Shreya Ghosh, Kalin Stefanov +5
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 Att…
Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation
Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi +1
We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, e…
Visual Representations of Physiological Signals for Fake Video Detection
Kalin Stefanov, Bhawna Paliwal, Abhinav Dhall
Realistic fake videos are a potential tool for spreading harmful misinformation given our increasing online presence and information intake. This paper presents a multimodal learni…
Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization
Zhixi Cai, Kalin Stefanov, Abhinav Dhall +1
Due to its high societal impact, deepfake detection is getting active attention in the computer vision community. Most deepfake detection methods rely on identity, facial attribute…
Webcam-based Eye Gaze Tracking under Natural Head Movement
Kalin Stefanov
This manuscript investigates and proposes a visual gaze tracker that tackles the problem using only an ordinary web camera and no prior knowledge in any sense (scene set-up, camera…
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition
Kalin Stefanov, Jonas Beskow, Giampiero Salvi
This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental pre…