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Depth prediction from a single monocular image is a challenging yet valuable task, as often a depth sensor is not available. The state-of-the-art approach \cite{Liu2016} combines a deep fully convolutional network (DFCN) with a conditional random field (CRF), allowing the CRF to correct and smooth the depth values estimated by the DFCN according to efficient contextual modeling.

Videos of complex events are difficult to represent solely as
bags of low level features. Increasingly, supervised concepts
or attributes are being employed as the intermediate representation
of such videos. We propose a probabilistic framework
that models the conditional relationships between the
concepts and events and devise an approximate yet tractable
solution to infer the posterior distribution to perform event
classification. Using noisy outputs of pre-trained concept detectors,
we learn semantic and visual dependencies between

Binary hashing is an established approach for fast, approximate image search. It maps a query image to a binary vector so that Hamming distances approximate image similarities. Applying the hash function can be made fast by using a circulant matrix and the fast Fourier transform, but this circulant hash function must be learned optimally from training data. We show that a previously proposed learning algorithm based on optimization in the frequency domain is suboptimal.

Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview

The success of deep learning—in the form of multi-layer neural networks — depends critically on the volume and variety of training data. Its potential is greatly compromised when training data originate in a geographically distributed manner and are subject to bandwidth constraints. This paper presents a data sampling approach to deep learning, by carefully discriminating locally available training samples based on their relative importance.

Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices. Among many pruning granularities, neuron level pruning will remove redundant neurons and filters in the model and result in thinner networks. In this paper, we propose a gradually global pruning scheme for neuron level pruning. In each pruning step,