In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …

Self-supervised learning algorithms based on instance discrimination train encoders to be
invariant to pre-defined transformations of the same instance. While most methods treat …

Most person re-identification (re-ID) approaches are based on supervised learning, which
requires intensive manual annotation for training data. However, it is not only …

Self-supervised learning aims to learn representations from the data itself without explicit
manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning-the …

This paper proposes a method to modify a traditional convolutional neural network (CNN)
into an interpretable CNN, in order to clarify knowledge representations in high conv-layers …

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect
to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a …

The field of object detection has made significant advances riding on the wave of region-
based ConvNets, but their training procedure still includes many heuristics and …

We present an unsupervised representation learning approach using videos without
semantic labels. We leverage the temporal coherence as a supervisory signal by formulating …

In this paper, we present an approach for learning a visual representation from the raw
spatiotemporal signals in videos. Our representation is learned without supervision from …