41,221 research outputs found
A Discriminatively Learned CNN Embedding for Person Re-identification
We revisit two popular convolutional neural networks (CNN) in person
re-identification (re-ID), i.e, verification and classification models. The two
models have their respective advantages and limitations due to different loss
functions. In this paper, we shed light on how to combine the two models to
learn more discriminative pedestrian descriptors. Specifically, we propose a
new siamese network that simultaneously computes identification loss and
verification loss. Given a pair of training images, the network predicts the
identities of the two images and whether they belong to the same identity. Our
network learns a discriminative embedding and a similarity measurement at the
same time, thus making full usage of the annotations. Albeit simple, the
learned embedding improves the state-of-the-art performance on two public
person re-ID benchmarks. Further, we show our architecture can also be applied
in image retrieval
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
Etsy is a global marketplace where people across the world connect to make,
buy and sell unique goods. Sellers at Etsy can promote their product listings
via advertising campaigns similar to traditional sponsored search ads.
Click-Through Rate (CTR) prediction is an integral part of online search
advertising systems where it is utilized as an input to auctions which
determine the final ranking of promoted listings to a particular user for each
query. In this paper, we provide a holistic view of Etsy's promoted listings'
CTR prediction system and propose an ensemble learning approach which is based
on historical or behavioral signals for older listings as well as content-based
features for new listings. We obtain representations from texts and images by
utilizing state-of-the-art deep learning techniques and employ multimodal
learning to combine these different signals. We compare the system to
non-trivial baselines on a large-scale real world dataset from Etsy,
demonstrating the effectiveness of the model and strong correlations between
offline experiments and online performance. The paper is also the first
technical overview to this kind of product in e-commerce context
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