Search Results for author:
Found 11 papers, 5 papers with code
Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency.
Learning Audio Concepts from Counterfactual Natural Language
Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text.
Knowledge-driven Scene Priors for Semantic Audio-Visual Embodied Navigation
We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects.
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Anomaly detection has many important applications, such as monitoring industrial equipment.
Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection
The fast and continuous growth in number and quality of deepfake videos calls for the development of reliable detection systems capable of automatically warning users on social media and on the Internet about the potential untruthfulness of such contents.
Video Face Manipulation Detection Through Ensemble of CNNs
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
Ranked #1 on
DeepFake Detection
on FaceForensics++
(using extra training data)
An In-Depth Study on Open-Set Camera Model Identification
To deal with this issue, in this paper, we present the first in-depth study on the possibility of solving the camera model identification problem in open-set scenarios.
A Counter-Forensic Method for CNN-Based Camera Model Identification
We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed.
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for example, by issuing warnings to the driver or acting actively on car's brakes, it helps decreasing the probability of injuries and human fatalities.
First Steps Toward Camera Model Identification with Convolutional Neural Networks
Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution.
Deep convolutional neural networks for pedestrian detection
Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics.
