The purpose of unsupervised person re-identification (Re-ID) is to improve the recognition performance of the model without using any labeled Re-ID datasets. Recently, camera differences and noisy labels have emerged as critical factors hindering the improvement of unsupervised Re-ID performance. To address these issues, we propose a camera style alignment (CSA) method. In CSA, we first devise the feature mean clustering (FM-clustering) algorithm, which is based on the average features for clustering to mitigate the impact of camera differences on the clustering results. Subsequently, we design dual-cluster consistency refinement (DCR), which assesses the reliability of pseudo-labels from the perspective of clustering consistency, thereby reducing the influence of noisy labels. In addition, we introduce style-aware invariance loss and camera-aware invariance loss to achieve camera style-invariant learning from different aspects. Style-aware invariance loss will improve the similarity between samples and their style-transferred counterparts, and camera-aware invariance loss will improve the similarity between positive samples of different cameras. The experimental results on the Market-1501 and MSMT17 datasets show that the performance of CSA exceeds the existing fully unsupervised Re-ID and unsupervised domain adaptation Re-ID methods.

The deep unfolding network (DUN) for image compressive sensing (ICS) integrates a traditional optimization algorithm with a neural network, providing clear interpretability and demonstrating exceptional performance. Nevertheless, the inherent paradigm of the DUN lies in the independent proximal mapping between iterations and the limited information flux, potentially constraining the mapping capability of the deep unfolding method. This paper introduces a Feature-Domain FISTA-Inspired Deep Unfolding Network (FDFI-DUN) for ICS. FDFI-DUN comprises a Feature-Domain Nesterov Momentum Module (FNMM), a Feature-Domain Gradient Descent Module (FGDM), and a Two-level Multiscale Proximal Mapping Module (TMPMM). Specifically, the Nesterov momentum term and gradient descent term in the FISTA are tailored to the feature domain, enhancing the information flux of the entire DUN and augmenting the feature information within and between iterations while maintaining clear interpretability. Furthermore, the TMPMM, encompassing intra-stage and inter-stage components, is designed to further augment the information flux and effectively utilize multiscale feature information for reconstructing image details. Extensive experimental results demonstrate that the proposed FDFI-DUN surpasses state-of-the-art methods in both quality and vision. Our codes are available at: https://github.com/giant-pandada/FDFI-DUN.

For landslides, a serious natural disaster, how to accurately locate the landslide area is crucial for disaster mitigation and relief work. In view of the complex situation of landslides and the difficulty of traditional methods in quickly and accurately determining the area where landslides occur, this paper proposes a multi-scale feature recognition algorithm for landslide images (MF-L-UNet++) by analyzing the characteristics of landslides and common semantic segmentation networks. MF-L-UNet++ is based on UNet++ with the following modifications. First, the Dual Large Feature Fusion Selective Kernel Attention (DLFFSKA) module is employed to eliminate the interference of background in model recognition and enhance the accuracy of landslide location capture. Second, the Same Scale Lightweight Kernel Prediction (SSLKP) is designed to achieve a significant reduction in the number of parameters while reducing the loss of convolutional feature information and position offset. Third, Large Kernel Content Aware Recombination Upsample (LKCARU) is presented to enhance the model’s capacity to delineate the boundaries and details of the landslide, thereby facilitating more precise segmentation outcomes. Finally, Atrous Spatial Pyramid Pooling (ASPP) is introduced to address the issue of inadequate coverage and fusion of multi-scale information following the utilization of multiple modules, enabling the model to fully integrate global context information. The experimental results showed that on the expanded Bijie Landslide Dataset, the algorithm proposed in this study achieved an improvement of 3.68%, 1.29%, and 1.59% in IoU, Precision, and F1-score, respectively, compared to the UNet++ algorithm, while Params and Loss decreased by 0.86M and 0.05, respectively. Compared to other commonly used segmentation methods, the detection performance of the model in this paper is at the optimal level.

Multi-modal entity alignment (MMEA) endeavors to ascertain whether two multi-modal entities originating from distinct knowledge graphs refer to a congruent real-world object. This alignment is a pivotal technique in knowledge graph fusion, which aims to enhance the overall richness and comprehensiveness of the knowledge base. Existing mainstream MMEA models predominantly leverage graph convolutional networks and pre-trained visual models to extract the structural and visual features of entities, subsequently proceeding to integrate these features and conduct similarity comparisons. However, given the often suboptimal quality of multi-modal information in knowledge graphs, reliance solely on traditional visual feature extraction methods and the extraction of visual and structural features alone may result in insufficient semantic information within the generated multi-modal joint embeddings of entities. This limitation could potentially hinder the accuracy and effectiveness of multi-modal entity alignment. To address the above issues, we propose MSEEA, a Multi-modal Entity Alignment method based on Multidimensional Semantic Extraction. First, MSEEA fine-tunes a large language model using preprocessed entity relationship triples, thereby enhancing its capacity to analyze latent semantic information embedded in structural triples and generate contextually rich entity descriptions. Second, MSEEA employs a combination of multiple advanced models and systems to extract multidimensional semantic information from the visual modality, thereby circumventing the feature quality degradation that can occur with reliance solely on pre-trained visual models. Finally, MSEEA integrates different modal embeddings of entities to generate multi-modal representations and compares their similarities. We designed and executed experiments on FB15K-DB15K/YAGO15K, and the outcomes demonstrate that MSEEA outperforms traditional approaches, achieving state-of-the-art results.

In the Autonomous Driving (AD) scenario, accurate, informative, and understandable descriptions of the traffic conditions and the ego-vehicle motions can increase the interpretability of an autonomous driving system to the vehicle user. End-to-end free-form video captioning is a straightforward vision-to-text task to address such needs. However, insufficient real-world driving scene descriptive data hinders the performance of caption generation under a simple supervised training paradigm. Recently, large-scale Vision-Language Pre-training (VLP) foundation models have attracted much attention from the community. Tuning large foundation models on task-specific datasets becomes a prevailing paradigm for caption generation. However, for the application in autonomous driving, we often encounter large gaps between the training data for VLP foundation models and the real-world driving scene captioning data, which impedes the immense potential of VLP foundation models. In this paper, we present to tackle this problem via a unified framework for cross-lingual cross-domain vision-language tuning empowered by Machine Translation (MT) techniques. We aim to obtain a captioning system for driving scene caption generation in Japanese from a domain-general and English-centric VLP model. The framework comprises two core components: (i) bidirectional knowledge distillation by MT teachers; (ii) fusing objectives for cross-lingual fine-tuning. Moreover, we introduce three schedulers to operate the vision-language tuning process with fusing objectives. Based on GIT, we implement our framework and verify its effectiveness on real-world driving scenes with natural caption texts annotated by experienced vehicle users. The caption generation performance with our framework reveals a significant advantage over the baseline settings.

Automatic Essay Scoring is a crucial task aimed at alleviating the workload of essay graders. Most of the previous studies have been focused on English essays, primarily due to the availability of extensive scored essay datasets. Thus, it remains uncertain whether the models developed for English are applicable to smaller-scale Japanese essay datasets. Recent studies have demonstrated the successful application of BERT-based regression and ranking models. However, downloadable Japanese GPT models, which are larger than BERT, have become available, and it is unclear which types of modeling are appropriate for Japanese essay scoring. In this paper, we explore various aspects of modeling using GPTs, including the type of model (i.e., classification or regression), the size of the GPT models, and the approach to training (e.g., learning from scratch versus conducting continual pre-training). In experiments conducted with Japanese essay datasets, we demonstrate that classification models combined with soft labels are more effective for scoring Japanese essays compared to the simple classification models. Regarding the size of GPT models, we show that smaller models can produce better results depending on the model, type of prompt, and theme.

Cross-lingual summarization (CLS) simplifies obtaining information across languages by generating summaries in the target language from source documents in another. State-of-the-art neural summarization models typically rely on training or fine-tuning with extensive corpora. Nonetheless, applying these approaches in practical industrial scenarios poses challenges due to the scarcity of annotated data. Recent research utilizes large language models (LLMs) to generate superior summaries by extracting fine-grained elements (entities, dates, events, and results) from source documents based on the Chain of Thought (CoT). Such an approach inevitably leads to the loss of fact-relationship across elements in the original document, thus hurting the performance of summary generation. In this paper, we not only substantiate the importance of the fact-relationship across elements for summary generation on the element-aware test sets CNN/DailyMail and BBC XSum but also propose a novel Cross-Lingual Summarization method based on Element Fact-relationship Generation (EFGCLS). Specifically, we break down the CLS task into three simple subtasks: though element fact-relationship generation extracts fine-grained elements in source documents and the fact-relationship across them; afterwards the monolingual document summarization leverages the fact-relationship and source documents to generate the monolingual summary; ultimately, the cross-lingual summarization via Cross-lingual Prompting (CLP) enhance the alignment between source language summaries and target language summaries. Experimental results on the element-aware datasets show that our method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +6.28/+1.22 in ROUGE-L, respectively.

With the development of neuroscience and psychology, the cerebellar role in higher-order functions has been increasingly recognized. Premature birth has an impact on cerebellar development and increases the risk of neurodevelopmental disorders. This study aimed to evaluate the development and alterations of glutamate levels and volumes in cerebellar subregions in preterm infants and investigate the relationship of glutamate and volumes. 70 preterm infants and 22 full-term infants underwent glutamate chemical exchange saturation transfer (GluCEST) and sampling perfection with application optimized contrasts using different flip angle evolutions (SPACE). Custom-written scripts in MATLAB were used to process GluCEST images to obtain glutamate levels, and volumes were obtained by ITK-SNAP. Both glutamate levels and volumes in cerebellar subregions in preterm infants were positively correlated with postmenstrual age. Furthermore, when compared to full-term infants, the glutamate levels of preterm infants at term-equivalent age were higher. No correlation was found between glutamate and volume. The metabolite and structure of preterm infants in cerebellar subregions were altered even in the absence of significant brain structure damage. These findings may help probe the pattern of brain maturation and identify potential neurodevelopmental disorders in preterm infants.

In this paper, we propose an automatic segmentation method for detecting lesion areas from full-screen Narrow Band Imaging (NBI) endoscopic image frames using deep learning for real-time diagnosis support in endoscopy. In existing diagnosis support systems, doctors need to actively align lesion areas to accurately classify lesions. Therefore, we aim to develop a real-time diagnosis support system combining an automatic lesion segmentation algorithm, which can identify lesions in full-screen endoscopic image. We created a dataset of over 8000 images and verified the detection performance of multiple existing segmentation model structures. We realized that there is a serious problem of missing detection dealing with images with small lesion. We analyzed the possible reason and proposed a method of using convolutional backbone network for downsampling to retain effective information, and conducted experiments with a model structure using Dense Block and U-Net. The experimental results showed that the detection performance of our structure showed superiority over other models for small lesions. At the same time, CutMix, a data augmentation method added to the model learning method to further improve detection performance, was proven to be effective. The detection performance achieved an accuracy of 0.8603 ± 0.006 when evaluated using F-measure. In addition, our model showed the fastest processing speed in experimental test, which will be advantageous in the subsequent development of processing system for real-time clinical videos.

In recent years, environmental sustainability and the reduction of CO₂ emissions have become significant research topics. To effectively reduce CO₂ emissions, recent studies have used deep learning models to provide precise estimates, but these models often lack interpretability. In light of this, our study employs an explainable neural network to learn fuel consumption, which is then converted to CO₂ emissions. The explainable neural network includes an explainable layer that can explain the importance of each input variable. Through this layer, the study can elucidate the impact of different speeds on fuel consumption and CO₂ emissions. Validated with real fleet data, our study demonstrates an impressive mean absolute percentage error (MAPE) of only 3.3%, outperforming recent research methods.

We propose a task scheduling method using resource interleaving and Reinforcement Learning (RL) for edge network system. We use resource interleaving to schedule edge node task forwarding to reduce task waiting delay on resources after being forwarded. We formulate a task scheduling optimization problem and use RL to ensure real-time policy. Simulations verify the proposed method’s effectiveness in task scheduling.

Hardware accelerators using fixed-point quantization efficiently run object detection neural networks, but high-bit quantization demands substantial hardware and power, while low-bit quantization sacrifices accuracy. To address this, we introduce an 8-bit quantization scheme, ASPoT8, which uses add/shift operations to replace INT8 multiplications, minimizing hardware area and power consumption without compromising accuracy. ASPoT8 adjusts quantified value distribution to match INT8’s accuracy. Tests on YOLOV3 Tiny and MobileNetV2 SSDlite show minimal mAP drops of 0.5% and 0.2%, respectively, with significant reductions in power (76.31%), delay (29.46%), and area (58.40%) over INT8, based on SMIC 40nm.

The past decade has witnessed the rapid development of Neural Machine Translation (NMT). However, NMT approaches tend to generate fluent but sometimes unfaithful translations of the source sentences. In response to this problem, we propose a new framework to incorporate the bilingual phrase knowledge into the encoder-decoder architecture, which allows the system to make full use of the phrase knowledge flexibly with no need to design complicated search algorithm. A significant difference to the existing work is that we obtain all the target phrases aligning to any part of the source sentence and learn representations for them before the decoding starts, which alleviates the hurt of invisibility of the future context in the standard autoregressive decoder, so that the generated target words can be decided more accurately with a global understanding. Extensive experiments on Japanese-Chinese translation task show that the proposed approach significantly outperforms multiple strong baselines in terms of BLEU scores, and verify the effectiveness of exploiting bilingual phrase knowledge for NMT.

Whereas vibrato is one of the most frequently used techniques to enrich vocal and musical instrument sounds, the performance of fine frequency estimation methods has not been studied much for vibrato tones. We present three models of synthetic vibrato tones and compare three DFT-based fine frequency estimation methods using the models, which are phase difference estimation (PDE), zero-padding method (ZPM), and corrected quadratically interpolated fast Fourier transform (CQIFFT). Experimental results show that CQIFFT and ZPM with a large number of padded zeroes are effective in the fine frequency estimation of vibrato tones. We also show an example of applying each method to a flute vibrato tone. We expect that the results will be helpful in choosing a fine frequency estimation method for DFT-based methods to analyze the frequencies of vibrato tones.