62 citations · 350 across the 33 of their papers we have counts for
8 papers · 1 filter
ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking
Tzoulio Chamiti, Leandro Di Bella, Adrian Munteanu +1
Tracking multiple objects based on textual queries is a challenging task that requires linking language understanding with object association across frames. Previous works typicall…
Interpretable Deep Multimodal Image Super-Resolution
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality. While existing de…
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge abo…
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
Pingfan Song, Xin Deng, João F. C. Mota +3
Real-world data processing problems often involve various image modalities associated with a certain scene, including RGB images, infrared images or multi-spectral images. The fact…
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism…
Distributed Coding of Multiview Sparse Sources with Joint Recovery
Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer +1
In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sou…
Multi-modal dictionary learning for image separation with application in art investigation
Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis +2
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals…
X-ray image separation via coupled dictionary learning
Nikos Deligiannis, João F. C. Mota, Bruno Cornelis +2
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation m…