Search Results for author:
Found 32 papers, 18 papers with code
Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia
Particularly, a similar Wikimedia image can be used to illustrate different articles, and the produced caption needs to be adapted to a specific context, therefore allowing us to explore the limits of a model to adjust captions to different contextual information.
MUST-VQA: MUltilingual Scene-text VQA
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion.
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement.
OCR-IDL: OCR Annotations for Industry Document Library Dataset
It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence.
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching
In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance.
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning
Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning.
Asking questions on handwritten document collections
This work addresses the problem of Question Answering (QA) on handwritten document collections.
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach
This paper presents a novel method for date estimation of historical photographs from archival sources.
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder.
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models).
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems.
Location Sensitive Image Retrieval and Tagging
People from different parts of the globe describe objects and concepts in distinct manners.
Text Recognition -- Real World Data and Where to Find Them
We present a method for exploiting weakly annotated images to improve text extraction pipelines.
RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
State of the art methods for text detection, recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets.
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Ranked #1 on
Fine-Grained Image Classification
on Con-Text
Exploring Hate Speech Detection in Multimodal Publications
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image.
ICDAR 2019 Competition on Scene Text Visual Question Answering
ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image.
Selective Style Transfer for Text
This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions.
Scene Text Visual Question Answering
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image.
Good News, Everyone! Context driven entity-aware captioning for news images
We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image.
Self-Supervised Visual Representations for Cross-Modal Retrieval
Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places.
Self-Supervised Learning from Web Data for Multimodal Retrieval
In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval.
Learning from #Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods
We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level.
Learning to Learn from Web Data through Deep Semantic Embeddings
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval.
TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces
We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration.
Self-supervised learning of visual features through embedding images into text topic spaces
End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible.
Improving Text Proposals for Scene Images with Fully Convolutional Networks
Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image.
A fine-grained approach to scene text script identification
Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images.
Improving patch-based scene text script identification with ensembles of conjoined networks
Instead of resizing input images to a fixed aspect ratio as in the typical use of holistic CNN classifiers, we propose here a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class.
Object Proposals for Text Extraction in the Wild
The use of Object Proposals techniques in the scene text understanding field is innovative.
A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction
Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs.
