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- Stanford UniversityUS33 papers
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35 papers · 2 filters
Efficient Super Resolution Using Binarized Neural Network
Yinglan Ma, Hongyu Xiong, Zhe Hu +1
Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and l…
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
Learning to Separate Multiple Illuminants in a Single Image
Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli +1
We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each i…
Learning Material-Aware Local Descriptors for 3D Shapes
Hubert Lin, Melinos Averkiou, Evangelos Kalogerakis +5
Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolut…
DeepLens: Shallow Depth Of Field From A Single Image
Lijun Wang, Xiaohui Shen, Jianming Zhang +5
We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we prop…
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Ning Xu, Linjie Yang, Yuchen Fan +6
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentatio…