Search Results for author:
Found 11 papers, 2 papers with code
Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning
The classification of distracted drivers is pivotal for ensuring safe driving.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion
This advance opens up new possibilities for generating captions that are more suitable for the training of both vision-language and captioning models.
Semi-supervised cross-lingual speech emotion recognition
Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques.
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques.
Composition and Style Attributes Guided Image Aesthetic Assessment
In this paper we propose a method for the automatic prediction of the aesthetics of an image that is based on the analysis of the semantic content, the artistic style and the composition of the image.
Disentangling Image Distortions in Deep Feature Space
In this work we take a further step in the direction of a broader understanding of such property by analyzing the capability of deep visual representations to intrinsically characterize different types of image distortions.
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition.
Aesthetics Assessment of Images Containing Faces
Recent research has widely explored the problem of aesthetics assessment of images with generic content.
On the Use of Deep Learning for Blind Image Quality Assessment
We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task.
