Versatile Denoising-Based Approximate Message Passing for Compressive Sensing.

Approximate message passing-based compressive sensing reconstruction has received increasing attention, the performance of which depends heavily on the ability of the denoising operator. However, most methods only employ an off-the-shelf denoising model as the denoising operator of the iteration solver, which imposes an unfavorable limit on reconstruction performance of compressive sensing. To solve the aforementioned issue, we propose a novel versatile denoising-based approximate message passing model, abbreviated as VD-AMP, for compressive sensing (CS) recovery. To be specific, we meticulously design a double encoder-decoder denoising network (DEDNet), which manifests the impressive performance in Gaussian denoising. Moreover, a fine-grained noise level division (FNLD) solution is proposed to release the potential of the well-designed DEDNet so as to improve the reconstruction performance. However, strengthening the denoiser alone fails to remove the distortion artifact of reconstruction images at low sampling rates. To alleviate the defect, we propose an anti-aliasing sampling (AS), which firstly maps the input image to a smoothing sub-space using the proposed DEDNet before vanilla sampling, reducing aliasing between high-frequency and low-frequency information on measurement. Extensive experiments on benchmark datasets demonstrate that the proposed VD-AMP significantly outperforms state-of-the-art CS reconstruction models by a large margin, e.g., up to 2 dB gains on PSNR.

Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning.

Zero-shot learning (ZSL) is a challenging task due to the lack of unseen class data during training. Existing works attempt to establish a mapping between the visual and class spaces through a common intermediate semantic space. The main limitation of existing methods is the strong bias towards seen class, known as the domain shift problem, which leads to unsatisfactory performance in both conventional and generalized ZSL tasks. To tackle this challenge, we propose to convert ZSL to the conventional supervised learning by generating features for unseen classes. To this end, a joint generative model that couples variational autoencoder (VAE) and generative adversarial network (GAN), called Zero-VAE-GAN, is proposed to generate high-quality unseen features. To enhance the class-level discriminability, an adversarial categorization network is incorporated into the joint framework. Besides, we propose two self-training strategies to augment unlabeled unseen features for the transductive extension of our model, addressing the domain shift problem to a large extent. Experimental results on five standard benchmarks and a large-scale dataset demonstrate the superiority of our generative model over the state-of-the-art methods for conventional, especially generalized ZSL tasks. Moreover, the further improvement of the transductive setting demonstrates the effectiveness of the proposed self-training strategies.

POI Summarization by Aesthetics Evaluation From Crowd Source Social Media.

Place-of-Interest (POI) summarization by aesthetics evaluation can recommend a set of POI images to the user and it is significant in image retrieval. In this paper, we propose a system that summarizes a collection of POI images regarding both aesthetics and diversity of the distribution of cameras. First, we generate visual albums by a coarse-to-fine POI clustering approach and then generate 3D models for each album by the collected images from social media. Second, based on the 3D to 2D projection relationship, we select candidate photos in terms of the proposed crowd source saliency model. Third, in order to improve the performance of aesthetic measurement model, we propose a crowd-sourced saliency detection approach by exploring the distribution of salient regions in the 3D model. Then, we measure the composition aesthetics of each image and we explore crowd source salient feature to yield saliency map, based on which, we propose an adaptive image adoption approach. Finally, we combine the diversity and the aesthetics to recommend aesthetic pictures. Experimental results show that the proposed POI summarization approach can return images with diverse camera distributions and aesthetics.