250 citations · 532 across the 72 of their papers we have counts for
5 papers · 2 filters
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Ehsan Abbasnejad +4
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various neural architectures, spanning Transformer-based models li…
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad +4
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box…
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
Iman Abbasnejad, Fabio Zambetta, Flora Salim +4
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images. Most current image-to-image transl…
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
Hai-Ming Xu, Lingqiao Liu, Hao Chen +2
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlab…
ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
Islam Nassar, Munawar Hayat, Ehsan Abbasnejad +2
Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as ad…