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Return of the Devil in the Details: Delving Deep into Convolutional Nets
TLDR
The latest generation of Convolutional Neural Networks (CNN) have achieved impressive results in challenging benchmarks on image recognition and object detection, significantly raising the interest of the community in these methods. Expand
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The devil is in the details: an evaluation of recent feature encoding methods
TLDR
A large number of novel encodings for bag of visual words models have been proposed in the past two years to improve on the standard histogram of quantized local features. Expand
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The AXES submissions at TRECVID 2013
TLDR
We use state-of-the-art local low-level descriptors for motion, image, and sound, as well as high-level features to capture speech and text and the visual and audio stream respectively. Expand
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Efficient retrieval of deformable shape classes using local self-similarities
TLDR
We present an efficient object retrieval system based on the identification of abstract deformable ‘shape’ classes using the self-similarity descriptor of Shechtman and Irani [13]. Expand
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VISOR: Towards On-the-Fly Large-Scale Object Category Retrieval
This paper addresses the problem of object category retrieval in large unannotated image datasets. Our aim is to enable both fast learning of an object category model, and fast retrieval over theExpand
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AXES at TRECVID 2012: KIS, INS, and MED
TLDR
The AXES project participated in the interactive instance search task (INS), the known-item searchtask (KIS), and the multimedia event detection task (MED) for TRECVid 2012. Expand
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Efficient On-the-fly Category Retrieval Using ConvNets and GPUs
TLDR
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. Expand
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On-the-fly learning for visual search of large-scale image and video datasets
TLDR
The objective of this work is to visually search large-scale video datasets for semantic entities specified by a text query. Expand
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Beyond Metadata: Searching Your Archive Based on its Audio-visual Content
TLDR
The EU FP7 project AXES aims at better understanding the needs of archive users and supporting them with systems that reach beyond the state-of-the-art. Expand
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The AXES research video search system
TLDR
We will demonstrate a multimedia content information retrieval engine developed for audiovisual digital libraries targeted at academic researchers and journalists. Expand
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