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Learning to Refine Object Segments

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Webly Supervised Semantic Segmentation

Sabine Süsstrunk, Bin Jin

We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common back- grounds, and realistic scenes of the ...

Ieee2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Sciences et Techniques de l’Ingénieur (STI)2017

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Mathieu Salzmann, Fatemehsadat Saleh

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using im ...

Springer Int Publishing Ag2016

A MultiPath Network for Object Detection

Pedro Henrique Oliveira Pinheiro

The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we ...

BMVA Press2016

From Image-level to Pixel-level Labeling with Convolutional Networks

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and natur ...

2015

Music Learning with Long Short Term Memory Networks

Florian François Colombo

Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long s ...

2015

Saliency Detection using Maximum Symmetric Surround

Sabine Süsstrunk, Radhakrishna Achanta

Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Recently, full-resolution salient maps that retain well-defined boundaries have attracted attention. In these map ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Robust Discriminative Keyword Spotting for Emotionally Colored Spontaneous Speech using Bidirectional LSTM Networks

In this paper we propose a new technique for robust keyword spotting that uses bidirectional Long Short-Term Memory (BLSTM) recurrent neural nets to incorporate contextual information in speech decoding. Our approach overcomes the drawbacks of generative H ...

2009