Tone mapping techniques, aiming to convert high dynamic range (HDR) images to high-quality low dynamic range (LDR) images for display, play a more crucial role in real-world vision systems with the increasing application of HDR images. However, obtaining paired HDR and high-quality LDR images is difficult, posing a challenge to deep learning based tone mapping methods. To over-come this challenge, we propose a novel zero-shot tone mapping framework that utilizes shared structure knowl-edge, allowing us to transfer a pre-trained mapping model from the LDR domain to HDR fields without paired training data. Our approach involves decomposing both the LDR and HDR images into two components: structural in-formation and tonal information. To preserve the original image's structure, we modify the reverse sampling process of a diffusion model and explicitly incorporate the struc-ture information into the intermediate results. Additionally, for improved image details, we introduce a dual-control network architecture that enables different types of conditional inputs to control different scales of the output. Experimental results demonstrate the effectiveness of our approach, surpassing previous state-of-the-art methods both qualitatively and quantitatively. Moreover, our model ex-hibits versatility and can be applied to other low-level vi-sion tasks without retraining. The code is available at https://github.com/ZSDM-HDRIZero-Shot-Diffusion-HDR.

Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is significantly hindered by laborious data annotation. Given this challenge, recent research has predominantly focused on two approaches: pretraining task-oriented models for event extraction and employing data augmentation techniques. These methods involve integrating external knowledge, semantic structures, or artificially generated samples using large language models (LLMs). However, their performances can be compromised due to two fundamental issues. Firstly, the alignment between the introduced knowledge and event extraction knowledge is crucial. Secondly, the introduction of data noise during the augmentation is unavoidable and can mislead the model’s convergence. To address these issues, we propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. Different from the existing methods introducing linguistic knowledge into data augmentation, an event aggregation network is established to introduce event knowledge into supervised learning by constructing adaptively-updated semantic representation for trigger and argument. For LLM-based augmentation, we design a new scheme including a multi-pattern rephrasing paradigm and a data-free composing paradigm. Instead of directly using augmentation samples in the supervised task, we introduce span-level contrastive learning to reduce data noise. Experiments on the ACE2005 and ERE-EN demonstrate that our proposed approach achieves new state-of-the-art results on both of the two datasets.

The hashtag is an effective tool to manage and distribute social media content in recent years. Most existing tag recommendation methods rely on user profiles to improve the F1 score by roughly 20%. In the final results, multimodal information accounts for 58% of the total, while user information accounts for 42%. However, these methods neither provide a sufficient fusion method of information across modalities nor ignore the visual information from the main sources of tags. In this paper, we propose a novel model entitled Transformer Network with Object Detection (TNOD), which utilizes the contextual semantics of entities combined with text and image information, forming a multi-layer attention mechanism. In particular, we use object detection to extract entities in images and fuse the relationship between entities, text, and images with a multi-layer attention mechanism. Experiment results validate the superiorities of our proposed scheme from the perspective of recall rate and precision rate.

Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery (LIB). However, accurately predicting the remaining useful life (RUL) of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method.

In the modern industry, the information has been sufficiently shared among the production equipment, intelligent subsystems, and mobile devices via advanced network technology. For this purpose, many challenges on plant-wide performance evaluation such as product quality prediction have been received considerable attention in complex industrial Internet of Things systems. In this article, an efficient and effective soft sensor based on the semisupervised parallel deepFM model is proposed for the product quality prediction. First, a label broadcasting method is presented to augment labeled samples from unlabeled samples. Then, a data binning method is introduced to discretize process variables for an unbiased estimation. Based on the modified deepFM model, quality information can be separately extracted from different components of the model while high- and low-dimensional features can be obtained. Manifold regularization is embedded into the back propagation algorithm, in which unlabeled samples issue can be further resolved. Experiments on a real-world dataset demonstrate the effectiveness and performance of the proposed methods.

Images taken outdoor often experience degradation due to the influence of haze. A lot of algorithms have been proposed to solve this problem. One kind of algorithms are based on some hand-crafted features, which often work only in the situations where those hand-crafted features are valid. There are also some algorithms, which use deep learning-based methods to recover clear images, but they depend on the 2D images, and their run time increases rapidly when the size of images gets larger. Moreover, these models need a large dataset to be trained. In this study, we proposed a novel way based on deep learning and histogram matching to overcome these common problems. Firstly, we develop a network with 1D ResNet structure to predict the histogram of a recovered image. Secondly, we match the histograms of the inputs to the outputs of the model, which are processed patch by patch, to get a series of clear patches. Finally, we use an image-guided filter to overcome the unnatural transition between patches. Experiments on both synthetic and real-world hazy images show that our method performs about 3% better in terms of SSIM(structural similarity index) and 15% better in terms of CIEDE2000 than some state-of-the-art methods on a synthetic hazy image dataset. Furthermore, our model runs faster than other deep-learning-based algorithms in our experiments by about 187% to 382%.

Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.