Xavier Giró

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The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures(More)
This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an assignment map, a compact representation that relates regions of an image with a visual word. We use the(More)
Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image-and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the(More)
This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single(More)
Acoustic event detection (AED) aims at determining the identity of sounds and their temporal position in audio signals. When applied to spontaneously generated acoustic events, AED based only on audio information shows a large amount of errors, which are mostly due to temporal overlaps. Actually, temporal overlaps accounted for more than 70% of errors in(More)
In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the(More)
Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis(More)
Acoustic events produced in meeting environments may contain useful information for perceptually aware interfaces and multimodal behavior analysis. In this paper, a system to detect and recognize these events from a multimodal perspective is presented combining information from multiple cameras and microphones. First, spectral and temporal features are(More)
This article presents GAT, a Graphical Annotation Tool based on a region-based hierarchical representation of images. The proposed solution uses Partition Trees to navigate through the image segments which are automatically defined at different spatial scales. Moreover, the system focuses on the navigation through ontologies for a semantic annotation of(More)
This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical(More)