Modeling semantic aspects for cross-media image indexing
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia.
Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...
We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...
Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...
Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...
We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations ...
State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are co ...
We present a texture-driven parametric snake for semi-automatic segmentation of a single and closed structure in an image. We propose a new energy functional that combines intensity and texture information. The two types of image information are balanced u ...
In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space ...
We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query access to the top-1 ranked unlabeled images from the database. Compared with que ...
Convolutional neural networks (CNNs) based approaches for semantic alignment and object landmark detection have improved their performance significantly. Current efforts for the two tasks focus on addressing the lack of massive training data through weakly ...