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In this paper, we propose a new approach for searching action proposals in unconstrained videos. Our method first produces snippet action proposals by combining state-of-the-art YOLO detector (Static YOLO) and our regression based RNN detector (Recurrent YOLO). Then, these short action proposals are integrated to form final action proposals by solving two-pass dynamic programming which maximizes actioness score and temporal smoothness concurrently.

Current video coders rely heavily on block-based motion compensation, which is known to accurately capture pure translation, but to (at best) approximate all other types of motion, such as rotation and zoom. Moreover, as motion vectors are obtained through pixel-domain block matching to optimize a rate-distortion cost, and do not necessarily represent the actual motion, the model should not be considered a proper sampling of the underlying pixel motion field.

A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network. While it learns to compress the images from a training set, the compression performance is very well generalized on images from a test set. Moreover, when fed with noisy versions of the test set, since it has priors from clean images, the network also efficiently denoises the test images during the reconstruction.

Optic Disc (OD) detection in retinal fundus images is a cru-cial stage for the automation of a screening system in diabetic ophthalmology. Most researches for automatic localization of OD benefit the regions of vessels. In this paper, we present a fast and novel method based on the Circlet Transform to detect OD in digital retinal fundus images that doesn’t utilize the location of the vessels. First, each R, G and B band is enhanced using CLAHE method. Then, the enhanced image in RGB color space is converted to L*a*b one.

Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex man-made objects with different materials and fine-structured ob-jects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-to-end network for semantic labeling.