Search

Grid collages (GClg) of small image collections are popular and useful in many applications, such as personal album management, online photo posting, and graphic design. In this article, we focus on how visual effects influence individual preferences through various arrangements of multiple images under such scenarios. A novel balance-aware metric is proposed to bridge the gap between multi-image joint presentation and visual pleasure. The metric merges psychological achievements into the field of grid collage. To capture user preference, a bonus mechanism related to a user-specified special location in the grid and uniqueness values of the subimages is integrated into the metric. An end-to-end reinforcement learning mechanism empowers the model without tedious manual annotations. Experiments demonstrate that our metric can evaluate the GClg visual balance in line with human subjective perception, and the model can generate visually pleasant GClg results, which is comparable to manual designs.

Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce adaptive exemplar filters to simplify the algorithm design, resulting in an automatic and efficient pruning approach called EPruner. Inspired by the face recognition community, we use a message-passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters. EPruner breaks the dependence on the training data in determining the “important” filters and allows the CPU implementation in seconds, an order of magnitude faster than GPU-based SOTAs. Moreover, we show that the weights of exemplars provide a better initialization for the fine-tuning. On VGGNet-16, EPruner achieves a 76.34%-FLOPs reduction by removing 88.80% parameters, with 0.06% accuracy improvement on CIFAR-10. In ResNet-152, EPruner achieves a 65.12%-FLOPs reduction by removing 64.18% parameters, with only 0.71% top-5 accuracy loss on ILSVRC-2012. Our code is available at https://github.com/lmbxmu/EPruner.

Transformers, the dominant architecture for natural language processing, have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance. Transformers are sequence-to-sequence models, which use a self-attention mechanism rather than the RNN sequential structure. Thus, such models can be trained in parallel and can represent global information. This study comprehensively surveys recent visual transformer works. We categorize them according to task scenario: backbone design, high-level vision, low-level vision and generation, and multimodal learning. Their key ideas are also analyzed. Differing from previous surveys, we mainly focus on visual transformer methods in low-level vision and generation. The latest works on backbone design are also reviewed in detail. For ease of understanding, we precisely describe the main contributions of the latest works in the form of tables. As well as giving quantitative comparisons, we also present image results for low-level vision and generation tasks. Computational costs and source code links for various important works are also given in this survey to assist further development.

A high‐performance electron transport layer (ETL)‐free planar fluorine‐doped tin oxide (FTO)/perovskite/hole‐transport material/Au solar cell was prepared. We revealed that a plasma‐cleaning pretreatment for FTO substrates could significantly improve the quality of perovskite films, leading to the promotion of charge separation, an increase in the electron‐transport rate, and a decrease in the recombination reaction at the FTO/perovskite interface. Finally, the efficiency of the cells was greatly improved. A power conversion efficiency of over 15 % and a fill factor of 0.68 were achieved under AM 1.5G 100 mW cm−2irradiation without the use of a compact n‐type metal‐oxide blocking layer. Direct growth: We present excellent performing electron selective layer (ESL)‐free perovskite solar cells (PSCs). The perovskite films with high electronic quality are directly grown on fluorine‐doped tin oxide (FTO) substrates, which are pretreated by ultraviolet–ozone and plasma cleaning. The ESL‐free PSCs based on the FTO substrates with plasma‐cleaning pretreatment achieve the best photovoltaic performance.

Graph convolutional networks (GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data. The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer, and Pubmed citation networks.