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In this paper, a tracking method based on sequential Bayesian inference is proposed. The proposed method focuses on solving both the problem of tracking under partial occlusions and the problem of non-rigid object tracking in real-time on a desktop personal computer (PC). The proposed method is mainly composed of two parts: (1) modeling the target object using elastic structure of local patches for robust performance; and (2) efficient hierarchical diffusion method to perform the tracking procedure in real-time. The elastic structure of local patches allows the proposed method to handle partial occlusions and non-rigid deformations through the relationship among neighboring patches. The proposed hierarchical diffusion method generates samples from the region where the posterior is concentrated to reduce computation time. The method is extensively tested on a number of challenging image sequences with occlusion and non-rigid deformation. The experimental results show the real-time capability and the robustness of the proposed method under various situations. [ABSTRACT FROM AUTHOR]

• The core research question of this paper is how can we find the image part described by a user, such that the output image crop will represent and preserve the caption information meanwhile result in an aesthetically pleasing output? • We have proposed a caption and aesthetics guided framework for cropping images according to the user's intention. Our framework is the first to account for the user's intention directly from the provided image caption. • We argue that the currently available image cropping and caption grounding datasets are not suitable for our description-based image cropping task. Therefore, we proposed a novel dataset with multiple ground truth bounding box annotations for each caption. • The experiments in Section 4.2 show that we can achieve better performance than the baseline methods for caption-based image cropping by re-proposing existing deep networks. We propose a novel optimization framework that crops a given image based on user description and aesthetics. Unlike existing image cropping methods, where one typically trains a deep network to regress to crop parameters or cropping actions, we propose to directly optimize for the cropping parameters by repurposing pre-trained networks on image captioning and aesthetic tasks, without any fine-tuning, thereby avoiding training a separate network. Specifically, we search for the best crop parameters that minimize a combined loss of the initial objectives of these networks. To make the optimization stable, we propose three strategies: (i) multi-scale bilinear sampling, (ii) annealing the scale of the crop region, therefore effectively reducing the parameter space, (iii) aggregation of multiple optimization results. Through various quantitative and qualitative evaluations, we show that our framework can produce crops that are well-aligned to intended user descriptions and aesthetically pleasing. [ABSTRACT FROM AUTHOR]