Search Results for author:
Found 8 papers, 4 papers with code
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
In literature, this task is often used as a pre-training objective to forge architectures able to jointly deal with images and texts.
Ranked #22 on
Cross-Modal Retrieval
on COCO 2014
CaMEL: Mean Teacher Learning for Image Captioning
Describing images in natural language is a fundamental step towards the automatic modeling of connections between the visual and textual modalities.
From Show to Tell: A Survey on Deep Learning-based Image Captioning
Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation.
Learning to Select: A Fully Attentive Approach for Novel Object Captioning
In this paper, we present a novel approach for NOC that learns to select the most relevant objects of an image, regardless of their adherence to the training set, and to constrain the generative process of a language model accordingly.
A Novel Attention-based Aggregation Function to Combine Vision and Language
The joint understanding of vision and language has been recently gaining a lot of attention in both the Computer Vision and Natural Language Processing communities, with the emergence of tasks such as image captioning, image-text matching, and visual question answering.
Meshed-Memory Transformer for Image Captioning
Transformer-based architectures represent the state of the art in sequence modeling tasks like machine translation and language understanding.
Ranked #2 on
Image Captioning
on MS COCO
Artpedia
As vision and language techniques are widely applied to realistic images , there is a growing interest in designing visual-semantic models suitable for more complex and challenging scenarios.
A Deep Learning based approach to VM behavior identification in cloud systems
The experiments compare our proposal with state-of-the-art solutions available in literature, demonstrating that our proposal achieve better performance.
