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Image to Video Domain Adaptation Using Web Supervision
Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain.
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
Our approach significantly outperforms the state-of-the-art on the DeepFashion dataset.
The Shape-Time Random Field for Semantic Video Labeling
In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos.
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
Although the CRF is a good baseline labeler, we show how an RBM can be added to the architecture to provide a global shape bias that complements the local modeling provided by the CRF.
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