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Deep Face Recognition

It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
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VGGFace2: A Dataset for Recognising Faces across Pose and Age

A new large-scale face dataset named VGGFace2 is introduced, which contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject, and the automated and manual filtering stages to ensure a high accuracy for the images of each identity are described.
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Cats and dogs

These models are very good: they beat all previously published results on the challenging ASIRRA test (cat vs dog discrimination) when applied to the task of discriminating the 37 different breeds of pets, and obtain an average accuracy of about 59%, a very encouraging result considering the difficulty of the problem.
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Fisher Vector Faces in the Wild

This paper shows that Fisher vectors on densely sampled SIFT features are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark, and shows that a compact descriptor can be learnt from them using discriminative metric learning.
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Template Adaptation for Face Verification and Identification

A surprising result is shown, that perhaps the simplest method of template adaptation, combining deep convolutional network features with template specific linear SVMs, outperforms the state-of-the-art by a wide margin.
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A Compact and Discriminative Face Track Descriptor

This work proposes a novel face track descriptor, based on the Fisher Vector representation, and demonstrates that it has a number of favourable properties, including compact size and fast computation, which render it very suitable for large scale visual repositories.
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The truth about cats and dogs

This approach proposes to use the template-based model to detect a distinctive part for the class, followed by detecting the rest of the object via segmentation on image specific information learnt from that part, and achieves accuracy comparable to the state-of-the-art on the PASCAL VOC competition, which includes other models such as bag- of-words.
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Total Cluster: A person agnostic clustering method for broadcast videos

The extent to which faces can be clustered automatically without making an error is explored, and an extension of the clustering method to entire episodes using exemplar SVMs based on the negative training data automatically harvested from the editing structure is proposed.
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Pointly-Supervised Instance Segmentation

The existing instance segmentation models developed for full mask supervision can be seamlessly trained with point-based supervision collected via the proposed point annotation scheme, which is approximately 5 times faster than annotating full object masks, making high-quality instance segmentations more accessible in practice.
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The AXES submissions at TRECVID 2013

The authors' INS, MER, and MED systems, which use systems based on 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, are described.
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