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Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well.

With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information.

In this paper, we propose a novel vehicle re-identification method based on a Deep Joint Discriminative Learning (DJDL) model, which utilizes a deep convolutional network to effectively extract discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, we design a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks. The whole network is optimized jointly via a specific batch composition design.

The binary partition tree (BPT) is a hierarchical data-structure that
models the content of an image in a multiscale way. In particular,
a cut of the BPT of an image provides a segmentation, as a partition
of the image support. Actually, building a BPT allows for
dramatically reducing the search space for segmentation purposes,
based on intrinsic (image signal) and extrinsic (construction metric)
information. A large literature has been devoted to the construction
on such metrics, and the associated choice of criteria (spectral, spatial,