Wei Li

The exponential growth of multimedia data in today’s highly digitized, mobile-centric society poses formidable challenges to efficient data storage and rapid information retrieval. To overcome these bottlenecks, we introduce ViT-LSH, a highly optimized algorithm that seamlessly integrates Vision Transformer (ViT) with locality-sensitive hashing (LSH) to deliver unprecedented image matching accuracy and retrieval speeds. The superiority of ViT-LSH stems from its dual-engine architecture. First, the ViT module revolutionizes feature extraction by dividing images into fixed-size patches and projecting them into token sequences without relying on down-sampling. This perfectly preserves the original image resolution and achieves exceptional global information modeling for semantic segmentation. Second, the LSH module drastically minimizes space consumption and accelerates query processing by mapping proximate data points to identical hash values. Evaluated against five existing baseline schemes, our LSH approach demonstrates vastly superior time and space efficiency while guaranteeing highly accurate approximate nearest neighbor queries. The proposed algorithm follows a streamlined, robust pipeline: image preprocessing (e.g., denoising) and segmentation, followed by ViT-driven feature extraction, dimensionality reduction, and LSH-based hash coding. By utilizing Hamming distance for direct similarity calculation, the system achieves ultra-fast sorting and matching. Extensive experimental results unequivocally validate that ViT-LSH provides a massive leap forward in both the computational efficiency and precision of large-scale image matching.

Action Quality Assessment (AQA)—the task of evaluating how well human actions are performed—is essential in domains such as sports and medicine. Existing AQA methods typically rely on score regression following feature extraction but often neglect the ambiguity inherent in extracted features. In this work, we introduce a novel AQA framework that incorporates a modified attention module to better capture relevant information. Our approach segments video data into clips, extracts features using the I3D network, and applies attention mechanisms to highlight salient features while suppressing irrelevant ones. To assess feature quality, we employ score distribution regression and propose an uncertainty-aware score distribution learning strategy that models features as Gaussian distributions. We further leverage Variational Autoencoders (VAEs) to capture complex latent representations and quantify uncertainty. Extensive experiments on the MTL-AQA and JIGSAWS datasets demonstrate the effectiveness and robustness of our proposed method.

The progress of Internet technology and the portability of mobile communication equipment have provided essential convenience for building an information society. Today, with networked and digital platforms as an important way of socializing, the information carrier is more based on images, videos, etc. The explosive growth of these multimedia data poses a huge challenge to efficient data storage and fast information retrieval. We design an algorithm combining Locality-sensitive Hashing (LSH) and Vision Transformer (ViT) to greatly improve the image matching rate and accuracy, and the algorithm is called ViT-LSH. The principle of LSH is to form the same hash value for points with similar distances in the data set. The results show that the LSH algorithm reduces space consumption and improves query processing efficiency compared with the other five schemes. Therefore, the LSH algorithm can help to quickly get the approximate nearest neighbor query results in a probabilistic manner, thereby ensuring the accuracy of the query results and the efficiency of time and space. The ViT divides the image into fixed-size image blocks ( Patch), uses Linear projection to project the Patch flatten to the specified dimension, gets the Tokens sequence, and adopts the sequence as the input of the feature to realize a new segmentation mode. The ViT does not employ down-sampling so that the image resolution is not reduced. The modeling of global information is a new semantic segmentation mode. The steps of ViT-LSH are as follows: first, preprocess the image, such as denoising, then segment the image, then extract features by ViT, and then reduce features' dimension. The LSH performs hash coding on the image features. After calculating the image's hash code, the image's similarity can be directly calculated by the Hamming distance, sorted according to the similarity, and the matching results are obtained. The experimental results exhibit that ViT-LSH can greatly enhance the efficiency and accuracy of image matching.

Focusing on the discovery of the value of in-hospital electronic medical record data for the three "chronic diseases" of diabetes, liver disease and hypertension, it provides data support for improving the hospital's "patient-centered" service level. Through web crawler technology, word frequency analysis technology, WeChat applet development technology, etc., we complete the design and development of big data systems such as data collection, preprocessing, analysis, and visualization, and tap the potential value of ten-year electronic medical record data. The standardized data collation platform and the development of the "Community Online" WeChat applet were completed. The original html data was standardized and stored in a relational database; through data mining, the distribution rules of occupation, age, gender, etc. of regional chronic diseases were found; through word frequency analysis, three kinds of chronic disease admission symptoms, treatment medication and discharge life suggestions were found hot word. Taking the system as the carrier, and through the research on the value discovery of Electronic Medical Records (EMR) data, a systematic chronic disease service system from health warning to admission treatment to discharge tracking has been built for patients with diabetes, liver disease and hypertension. Provide decision-making support for "early warning, early detection, early diagnosis, and early treatment" of chronic diseases and regional improvement of comprehensive management of chronic diseases and scientific treatment.

Based on the generative adversarial network, this paper proposes a method of colorizing gray images without supervision and applies it to the field of the automatic coloring of gray comic sketches to solve the problem of the high production cost of color comics and the large consumption of human resources. The method proposed in this paper mainly improves the classical generative adversarial network model in the following aspects: first, the idea of the residual network is added to the builder model and the discriminator model to ensure that the training of the model can develop in the right direction; secondly, add a gradient penalty term to the loss function of the discriminator to accelerate the convergence of the model and speed up the iteration; at the same time, the activation function in the original model is changed to the Mish activation function, so that the information flowing through the network model has higher accuracy and better generalization. This paper trained the model of the Anime Sketch Colorization Pair dataset [1] from Kaggle, and the final experimental results show that the method is practical and feasible.

In this paper, we propose a similar image retrieval algorithm based on feature fusion and locality-sensitive hash to address the problems of inadequate representation of image content by individual features and long retrieval time for massive image data. The fusion of global features and attention features makes the image features have both color structure and semantic information, which can better characterize the image content. In the image retrieval stage, the locality-sensitive hash is used to hash encode the image features, the cosine similarity is used as the similarity measure, and finally, the index is built to improve the retrieval efficiency. The similar image retrieval algorithm proposed in this paper has improved the average finding accuracy and recall rate on Caltech 256 and Corel5k datasets compared with other methods, and the retrieval time is greatly reduced.