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Semantic Attribute Matching Networks

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KNNs of Semantic Encodings for Rating Prediction

Léo Jules Laugier

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by sem ...

New York2023

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Yiyu Wang, Mengshi Qi

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multi-scale sequential network for semantic text segmentation and localization

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

2020

Joint Learning of Semantic Alignment and Object Landmark Detection

Seungryong Kim

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 ...

IEEE2019

Multiple Hypothesis Semantic Mapping for Robust Data Association

Roland Siegwart

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomou ...

2019

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

Jean-Philippe Thiran, Hazim Kemal Ekenel, Mohammad Saeed Rad

By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart. Although such objective functions generate near-ph ...

IEEE COMPUTER SOC2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

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, ...

EPFL2019

Message Distortion in Information Cascades

Robert West, Manoel Horta Ribeiro, Kristina Gligoric

Information diffusion is usually modeled as a process in which immutable pieces of information propagate over a network. In reality, however, messages are not immutable, but may be morphed with every step, potentially entailing large cumulative distortions ...

ASSOC COMPUTING MACHINERY2019

Unleashing the power of semantic text analysis: a complex systems approach

Andrea Martini

In the present information era, a huge amount of machine-readable data is available regarding scientific publications. Such unprecedented wealth of data offers the opportunity to investigate science itself as a complex interacting system by means of quanti ...

EPFL2018

Hexagons, Satellites and Semantic Background

Dario Rodighiero

The presentation is focused on a visual method that allows for a hexagonal arrangement in network visualization. Hexagonal tilling is a way to enrich the betweenness of nodes in order to enrich the information that a network visualization can convey. What ...

2018