State-of-the-art super-resolution (SR) methods commonly assume that the downscaling kernel (the point spread function of the camera) is a Gaussian kernel. Therefore, the methods are particularly vulnerable to solving the problem of blind SR, which deals with a real low-resolution (LR) image that does not follow the assumption. Recently, to address this issue, several internal learning-based methods, which train an image-specific network using a single input image, have been introduced. In this approach, the blind SR is modeled as a two-stage optimization problem, which conducts downscaling kernel estimation followed by SR network training with the estimated kernel. In this letter, we assume that not only the estimated kernel can be employed for SR network training, but also the super-resolved image can contribute to downscaling kernel estimation. To that end, we propose a unified internal learning-based blind SR method that jointly trains two image-specific networks for 1) downscaling together with kernel estimation and 2) SR. More specifically, we train the two networks to minimize a dual back-projection loss; the SR network is trained to reconstruct a given input image from the LR image generated by the downscaling network, and the downscaling network is trained to downscale the high-resolution image generated by the SR network to be as close as possible to the given input image. Through the complementary training, the downscaling kernel estimation becomes more accurate, resulting in better SR performance. In the experiment, we show that the proposed method outperforms previous two-stage internal learning-based methods in terms of both SR performance and efficiency.

Recent temporal action proposal generation approaches have suggested integrating segment-and snippet score-based methodologies for producing proposals with high recall and accurate boundaries. In this paper, different from such a hybrid strategy, we focus on the potential of the snippet score-based approach. Specifically, we propose a new snippet score-based method, named Snippet Relatedness-based Generator (SRG), with a novel concept of “snippet relatedness”. The snippet relatedness represents which snippets are related to a specific action instance. To effectively learn this snippet relatedness, we present “pyramid non-local operations” for locally and globally capturing long-range dependencies among snippets. By employing these components, SRG first produces a 2D relatedness score map that enables the generation of various temporal intervals reliably covering most action instances with high overlap. Then, SRG evaluates the action confidence scores of these temporal intervals and refines their boundaries to obtain temporal action proposals. On THUMOS-14 and ActivityNet-1.3 datasets, SRG outperforms state-of-the-art methods for temporal action proposal generation. Furthermore, compared to competing proposal generators, SRG leads to significant improvements in temporal action detection.

In this paper, we present a novel grid encoding model for content-aware image retargeting. In contrast to previous approaches such as vertex-based and axis-aligned grid encoding models, our approach takes each horizontal/vertical distance between two adjacent vertices as an optimization variable. Upon this difference-based encoding scheme, every vertex position of a target grid is subsequently determined after optimizing the one-dimensional values. Our quad edge-based grid model has two major advantages for image retargeting. First, the model enables a grid optimization problem to be developed in a simple quadratic program while ensuring the global convexity of objective functions. Second, due to the independency of variables, spatial regularizations can be applied in a locally adaptive manner to preserve structural components. Based on this model, we propose three quadratic objective functions. Note that, in our work, their linear combination guides a grid deformation process to obtain a visually comfortable retargeting result by preserving salient regions and structural components of an input image. Comparative evaluations have been conducted with ten existing state-of-the-art image retargeting methods, and the results show that our method built upon the quad edge-based model consistently outperforms other previous methods both on qualitative and quantitative perspectives.