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Found 13 papers, 4 papers with code
Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods
This paper investigates the use of Mean Opinion Score (MOS), a common image quality metric, as a user-centric evaluation metric for XAI post-hoc explainers.
On the stability, correctness and plausibility of visual explanation methods based on feature importance
In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w. r. t.
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022
Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos.
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance.
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
The goal is to detect and classify table tennis strokes in the videos, the first step of a bigger scheme aiming at giving feedback to the players for improving their performance.
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks
Given the task of image classification and a trained CNN, this work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, that assign an importance score to each pixel of the image based on its contribution to the decision of the network.
3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks
In the context of the study of sportsmen performances, a corpus of the particular actions of table tennis strokes is considered.
Features Understanding in 3D CNNs for Actions Recognition in Video
Features visualization is performed at the RGB and Optical flow branches of the network.
Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition
In many real-life tasks of application of supervised learning approaches, all the training data are not available at the same time.
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research.
Saliency Driven Object recognition in egocentric videos with deep CNN
In this paper we propose a Deep CNN approach and the general framework for recognition of objects in a real-time scenario and in an egocentric perspective.
Deep Learning for Saliency Prediction in Natural Video
We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity of the human visual system to residual motion.
Nested Graph Words for Object Recognition
In this paper, we propose a new, scalable approach for the task of object based image search or object recognition.
