Search Results for author:
Found 11 papers, 7 papers with code
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution
Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images.
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates
Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources.
Simple vs complex temporal recurrences for video saliency prediction
This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain.
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance.
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.
Temporal Saliency Adaptation in Egocentric Videos
This work adapts a deep neural model for image saliency prediction to the temporal domain of egocentric video.
Saliency Weighted Convolutional Features for Instance Search
This work explores attention models to weight the contribution of local convolutional representations for the instance search task.
Where is my Phone ? Personal Object Retrieval from Egocentric Images
This work presents a retrieval pipeline and evaluation scheme for the problem of finding the last appearance of personal objects in a large dataset of images captured from a wearable camera.
Bags of Local Convolutional Features for Scalable Instance Search
This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW).
Exploring EEG for Object Detection and Retrieval
We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.
