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A survey on metric learning for feature vectors and structured data
The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …
Multiple kernel learning for visual object recognition: A review
Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels
for a given recognition task. A number of studies have shown that MKL is a useful tool for …
for a given recognition task. A number of studies have shown that MKL is a useful tool for …
Visual recognition with deep nearest centroids
We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …
Deep sets
We study the problem of designing models for machine learning tasks defined on sets. In
contrast to the traditional approach of operating on fixed dimensional vectors, we consider …
contrast to the traditional approach of operating on fixed dimensional vectors, we consider …
On the automatic generation of medical imaging reports
Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-
writing can be error-prone for unexperienced physicians, and time-consuming and tedious …
writing can be error-prone for unexperienced physicians, and time-consuming and tedious …
Deep learning for extreme multi-label text classification
Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …
document its most relevant subset of class labels from an extremely large label collection …
Cnn-rnn: A unified framework for multi-label image classification
While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …
label image classification, it is important to note that most real world images contain multiple …
Learning fine-grained image similarity with deep ranking
Learning fine-grained image similarity is a challenging task. It needs to capture between-
class and within-class image differences. This paper proposes a deep ranking model that …
class and within-class image differences. This paper proposes a deep ranking model that …
Hierarchy parsing for image captioning
It is always well believed that parsing an image into constituent visual patterns would be
helpful for understanding and representing an image. Nevertheless, there has not been …
helpful for understanding and representing an image. Nevertheless, there has not been …
Is object localization for free?-weakly-supervised learning with convolutional neural networks
Successful visual object recognition methods typically rely on training datasets containing
lots of richly annotated images. Annotating object bounding boxes is both expensive and …
lots of richly annotated images. Annotating object bounding boxes is both expensive and …