Deep Randomized Ensembles for Metric Learning

Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In this work, we propose a novel, generalizable and fast method to define a family of embedding functions that can be used as an ensemble to give improved results. Each embedding function is learned by randomly bagging the training labels into small subsets. We show experimentally that these embedding ensembles create effective embedding functions. The ensemble output defines a metric space that improves state of the art performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes Retrieval and VehicleID.

Deep Feature Interpolation for Image Content Changes

We propose Deep Feature Interpolation (DFI), a new data-driven baseline for automatic high-resolution image transformation. As the name suggests, it relies only on simple linear interpolation of deep convolutional features from pre-trained convnets. We show that despite its simplicity, DFI can perform high-level semantic transformations like "make older/younger", "make bespectacled", "add smile", among others, surprisingly well - sometimes even matching or outperforming the state-of-the-art. This is particularly unexpected as DFI requires no specialized network architecture or even any deep network to be trained for these tasks. DFI therefore can be used as a new baseline to evaluate more complex algorithms and provides a practical answer to the question of which image transformation tasks are still challenging in the rise of deep learning.

Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"

Recovering shadows is an important step for many vision algorithms. Current approaches that work with time-lapse sequences are limited to simple thresholding heuristics. We show these approaches only work with very careful tuning of parameters, and do not work well for long-term time-lapse sequences taken over the span of many months. We introduce a parameter-free expectation maximization approach which simultaneously estimates shadows, albedo, surface normals, and skylight. This approach is more accurate than previous methods, works over both very short and very long sequences, and is robust to the effects of nonlinear camera response. Finally, we demonstrate that the shadow masks derived through this algorithm substantially improve the performance of sun-based photometric stereo compared to earlier shadow mask estimation.