Reviewerly/exHarmony huggingface dataset:
Research on Industrial Internet Traceability Technology Based on Blockchain

In recent years, the demand of Industrial Internet for traceability has changed from simply viewing product information to ensuring the safety, reliability and efficiency of product traceability. The current traceability method only takes the blockchain as a data storage tool and mainly relies on the method of accessing all blocks compared with cases to realize information traceability. It is not applicable to the scene of frequent query of product flow information. Combined with the characteristics of Industrial Internet, this paper proposes a mesh blockchain structure combined with product traceability system to ensure data security, improve data query speed as much as possible and reduce the storage of redundant data. On this basis, the state index tree is used to maintain the latest status of the product and reduce the time cost of updating the product status. When traceability is needed, it can be indexed quickly, which greatly improves the traceability efficiency.
Super Resolution of Airplane Target in Remote Sensing Images via A Multi-Degradation Model

In this paper, we argue that the degradation model assumed by existing work on super-resolution of remote sensing images deviates from those in real-world images. To address the above problem, this article proposes a super-resolution reconstruction method for unpaired airplanes in remote sensing images, which consists of a model that simulates the multiple degradations in real-world scenarios and a super-resolution generative adversarial network that generates high-resolution images. Specifically, the novel degradation model can cover a wide range of degradations in natural scenes, which contains diverse factors of additive Gaussian noise, Poisson noise, Brown Gaussian noise, Gaussian blur, etc. Experiments are conducted on the airplane targets in the Gaofen challenge dataset, and the results demonstrate the superiority of our method compared to state-of-the-art methods, especially in raw remote sensing images with multiple noise and blur, which may be preferred in other practical satellite applications with harsh circumstances.
UFI: A Unified Feature Interaction Framework for Multi-Label Image Classification

Multi-label image classification (MLIC) is a more challenging task compared with single-label image classification due to multiple concepts targets, and complex visual relationships should be formulated. Convolutional Neural Network (CNN) and Visual Transformer (ViT) have shown superior performance in local and global feature representations, respectively. However, the interactions between local and global features are neglected in current works. To further formulate the critical interactions, this paper designs a Unified Feature Interaction (UFI) framework, aiming to integrate the selected local features with global features based on CNN and ViT, simultaneously. The proposed UFI includes two key modules: Class-Related Feature Selection (CRFS) and Feature Interaction Attention (FIA) modules. Specifically, according to the activation map, CRFS selects target regions by the preliminary calculation of predicted scores. FIA enables the significant local-global feature interaction based on the selected target regions and whole image. We initially attempted to interact with local and global features for multi-label image classification. UFI provides a stable improvement over the baseline and produces a new state-of-the-art result on MS-COCO and VOC2007.
Deep Category-Aware Hashing for Object Retrieval in Multi-Label Image

Hashing learning for category-aware object retrieval in multi-label image is a challenging topic, in which the user is only interested in a certain object included in the query image rather than the entire image. Thus, it aims to find images which contain object similar to the interested one. However, previous hashing methods pre-select plenty of bounding box proposals or involve multiple independent steps to generate object-level representation, which may be suboptimal. In this paper, we propose a lightweight yet effective end-to-end deep category-aware hashing(DCAH) framework, which can generate individual hash code for each object included in the image by image-level label information, of which the key point is that it can directly localize object region with the assistance of category attention map. Extensive experiments on four benchmark datasets have demonstrated that our method achieves promising improvements on category-aware object retrieval results over the state-to-the-art methods.
Learning Data Hallucination and Reciprocal Guidance for Underwater Depth Estimation and Color Correction

Underwater vision is typically more difficult to tackle than open-air vision due to the degraded visibility and geometrical distortion, which impedes the development of underwater machine vision. Hence, we propose a joint depth estimation and color correction framework for underwater monocular images via data hallucination and reciprocal guidance learning. Specifically, due to the lack of labeled underwater data, we first design a data hallucination network to translate terrestrial images to multi-style synthetic underwater images while retaining the scene structure of terrestrial images from a single-source-multi-target perspective, benefiting the effective training of the joint tasks. Then, considering the strong connection between both tasks, we design a collaborative network to learn the reciprocal guidance between tasks from a multi-task perspective, thus improving the performance of each task. The whole framework can be trained end-to-end, and performs favorably against state-of-the-art methods in both depth estimation and color correction tasks.
Connect to the Past: Graph-Based Latent Estimation for Blind Super-Resolution

In this paper, we focus on blind super-resolution, which is a challenging problem due to the unknown degradation process. Previous methods assume a simplified degradation model and fail to generalize to multiple real-world scenarios. We reformulate the image degradation with a more general assumption and embed it as a degradation latent vector. To predict the latent, we first construct a correlation graph between input LR images and pre-sampled LR-HR image pairs. And then through a graph propagation, the degradation latent is obtained, aggregating valuable information from the graph. We name the graph module as GALE (Graph-bAsed Latent Estimation), which is referred as a connection to the past. Furthermore, we design a novel blind SR framework GALE-SR. Extensive experiments on synthetic and real-world images show that the proposed GALE-SR can provide visually favorable and state-of-the-art performance in blind SR problem.
Diverse Image Colorizaiton via Deep Color Prior and Triplet Latent Regularization

Automatic image colorization has made tremendous progress in recent years. However, previous methods rarely consider the inherent diversity of natural images, and existing diverse colorization approaches still suffer from the following two limitations. i) The diversity of generated images is limited. ii) Some inevitable artifacts substantially degrade the quality of colorization results. To cope with such problems, we propose a novel diverse image colorization network based on vanilla GAN, which extracts the deep color priors through an initial colorization network first, and then utilizes these priors to modulate the diverse generation to achieve high-fidelity colorization outputs. In addition, a novel triplet latent regularization is proposed to constrain the correspondence between latent codes and generated images, which efficiently alleviates mode collapse and encourages more diverse colorization results. Extensive experiments on three benchmark datasets demonstrate the superiority of our method over existing diverse image colorization models.
Implementing TBT in Online English Class for Indonesian Domestic Workers

This paper addresses teaching English to middle-aged learners who are the members of JPPRI (Jaringan Perempuan Pekerja Rumahan Indonesia – Indonesian Domestic Worker Network) as a part of community development. The 45 participants were not familiar with online classes and most of them were unable to spend time for self-learning apart from the class session; thus, the instructors needed to come up with teaching strategies to deal with the students' condition. This study attempted to solve problems related to students' low performance and participation by implementing TBT (Task-based Teaching) specifically designed to be delivered via WhatsApp group. The researchers conducted two cycles of action research and the altered teaching approach showed satisfactory results. The learners' performance and participation increased steadily after the teachers implemented the TBT. We conclude that the TBT model in this study is useful for online classes using WhatsApp application; repetition, elaboration, and learning duration are essential in the TBT model for middle-aged learners to attain the targeted learning outcomes.
Individual Property Inference Over Collaborative Learning in Deep Feature Space

Collaborative learning is used in multi-media applications to distribute computing tasks and data storage over multiple sites. Recent studies found that private data information can be derived from model updates between the server and clients. Yet, previous methods are limited by their capabilities of privacy inference in more general and practical situations. In this paper, we propose a novel property inference method in the deep feature space to overcome those limitations. In particular, our method can make inference decisions on the level of individual examples instead of a batch of examples. We can simultaneously perform multiple property inference attacks without the need of image reconstruction. The proposed method is evaluated on several image benchmark datasets, which demonstrates significant improvement of inference accuracy even in the presence of privacy protection schemes.
Graph-Based Structural Attributes for Vehicle Re-Identification

Vehicle re-identification (Re-ID), which aims to identify the same vehicle across different surveillance cameras, is a significant application in urban operation and security. Although the existing methods have noticed the importance of local features, near-duplicated cases are still hard to be handled. The reason lies that the attribute features and personalized structure of vehicles are often ignored. In this paper, we propose a graph-based structural attribute network (GSAN), which contains an attribute feature extraction module (AFEM) and a dual-grained structural relation module (DSRM). The AFEM aims to obtain attribute features of vehicles with structural information between attributes, and the DSRM aims to make the attribute features able to represent structural relation information between parts and attributes. The result on representative datasets shows that GSAN achieves competitive improvements over the state-of-the-art methods. We also collect a dataset of vehicle images with attribute annotations. Our dataset and code are released at https://github.com/HappyBoBo0331/GSAN.
Searching Models with Nested Attention for Blind Super-Resolution

Blind super-resolution task aims to restore low-resolution im“ages with unknown degradations to high-resolution counter-parts. Existing methods rely on degradations estimation to re-construct high-resolution images. However, they need human involvement to obtain the best results as they treat unknown types of degradations as known conditions and manually select corresponding trained models. Moreover, they cannot fully use estimated degradations and generate blurry artifacts as they ignore that the impact of degradations on images is re-lated to images contents. In this paper, we propose HIS-NEST which contains an automatic search strategy HIS and a net-work structure NEST. Specifically, to bypass manual partici-pation, HIS automatically selects the clearest image by esti-mating the qualities of generated images. Furthermore, NEST protects the connection between degradations and images by using no loss functions to limit the degradations estimation and analyzing degradations from the perspective of channel and space. Extensive experiments show that our method out-performs state-of-the-art methods.
Mr.CAN: Class-Aware Network with Multi-Relations for Temporal Action Detection

Recognizing action patterns and exploring multiple relations are vital for Temporal Action Detection (TAD) task, which aims at locating and classifying action segments in untrimmed videos. However, most existing methods attempt to build a general model to handle diverse actions, ignoring the huge difference between various classes. Besides, the exploration of temporal and semantic relations between different segments remains an ongoing challenge due to complex video content. In this paper, we contend that different action classes should be processed differently and thus design a new Class-Aware Mechanism to achieve accurate detection. Moreover, an effective module named Multi-relations Builder is proposed to establish temporal and semantic relations simultaneously. These two modules are integrated as Class-Aware Network with Multi-relations (MrCAN). In comprehensive experiments conducted on two benchmarks, it out-performs all other current methods and achieves state-of-the-art performance, improving the average mAP from 45.78% to 48.98% on THUMOS-14 and from 35.52% to 35.87% on ActivityNet-1.3 respectively. Furthermore, the well-designed Multi-relations Builder can also be used to boost some other existing methods.
DPGIR: SIFT Recovery from a Hazy Image

Scale-Invariant Feature Transform (SIFT) plays a significant role in vision applications. Indeed, even with the advent of deep learning, SIFT-based object re-identification has been found to be competitive in a variety of object and scene recognition challenges. This is a relatively robust method. But under severe imaging conditions like hazy, the haze greatly hinders SIFT detection, causing matching performance degradation. To solve the problem, in this paper we propose a deep learning SIFT recovery algorithm from a single hazy image with a two-task framework, namely, Difference of Gaussian (DoG) Pyramid and Gradient Image Recovery (DPGIR). One task is to recover the dehazed DoG pyramid of a hazy image, and the other is to recover its dehazed Gaussian gradients. This scheme to set different objectives for SIFT detection and description leads to very robust performance. Compared with state-of-the-art methods, experimental results demonstrate that our proposed algorithm recovers more SIFT key points.
Adaptive Estimation for Quantized Nonlinear Cascade System

In this paper, we introduce an adaptive estimation method for quantized nonlinear cascade system using moving window theory. Firstly, by force of the sub-decomposition technique, the considered system is transformed to a regression model without product term, in which the computational complexity is reduced. Secondly, by developing a moving window, the moving window output and moving window observation data are constructed, in which the estimation accuracy is lifted. Then, based on moving data, a filter is introduced to filter noise data, and to improve the bias estimation issue. Thirdly, by designing the forcing variables with adaptive attenuation coefficient, the estimation error data can be got which is used to develop estimator, in which it gives an optional scheme to design the adaptive estimator compared with the prediction error and observation error criterion. Finally, the example results demonstrate that the developed method is effective to achieve the parameter estimation for quantized nonlinear cascade system, and the has better performance compared with some estimators in term of estimation precision and convergence rate.
Content Adaptive Compressed Screen Content Video Quality Enhancement

In recent years, with the rise of various online learning plat-forms and game live broadcasting industry, screen content video is explosively increasing. There is an urgent demand to reduce the inevitable compression artifacts produced by traditional lossy compression method. However, there do not exist any research on compressed Screen Content Video (SCV) quality enhancement. Since SCV frames always con-sist of two main types of contents with different characteris-tics, i.e., text and graphic, a Content Adaptive model based on Two branches (CAT) is proposed in this paper. For en-hancing graphic, we utilize temporal information by motion compensation, while for enhancing text, we explore spatial relevance in horizontal and vertical directions to recover sharp edges. Furthermore, a content adaptive block is used to select content related features for the collaborative enhancement of both contents. We build a large SCV dataset compressed by H.266/VVC Test Model (VTM 12.1). On this dataset, experi-mental results demonstrate that the proposed method achieves the state-of-the-art performance on 10 different kinds of SCV test sequences.
Semi-Supervised 3D Medical Image Segmentation Using Shape-Guided Dual Consistency Learning

Popular semi-supervised image segmentation networks suf-fer from two problems: firstly, supervision is only performed on the last layer of the decoder, resulting in the network's weak generalization ability; secondly, the geometry shape constraints of targets are frequently disregarded in these net-works, leading to poor segmentation results. To address these issues, we propose a novel shape-guided dual consistency semi-supervised learning framework for 3D medical image segmentation. The proposed framework makes two contri-butions. Initially, we introduce a shape constraint to learn the shape representation, which converts the difference be-tween two networks into an unsupervised loss and lets the model learn the boundary information of targets. Addition-ally, we develop a deep-supervised knowledge transfer strat-egy that improves the generalization ability of the network without increasing extra computation costs. Experiments demonstrate that the proposed framework outperforms state-of-the-art semi-supervised methods due to the strong ability of knowledge mining on unlabeled data.
Multi Text Classification Model Based on BRET-CNN-BiLSTM

When the Bert pre-trained model is used for the Chinese text classification task, the internal parameters of the model are relatively fixed. In the process of training smaller data sets, it is easy to cause over-fitting phenomenon. Therefore, a multiple model structure based on Bert-CNN-BiLSTM is proposed. In this structure, the Bert model is used as the text information extractor, after the output features of Bert model, multi head attention and TEXT-CNN model are used for further feature information extraction, low-dimensional feature vectors with more dense semantic information are generated and spliced to improve the information entropy of text vectors. Finally, the BiLSTM with self-attention is used to extract the information of different words in text information and then classify the text information, and the loss function with Flooding mechanism is used to carry out back propagation to further prevent the over-fitting problem of the model on smaller data sets and enhance the generalization ability of the model. Compared with the traditional Bert, LSTM, TEXT-CNN models, the accuracy, precision, recall and F1 measure of this model are all better than those of traditional Bert, LSTM and TEXT-CNN models. Experiments show that the model can effectively extract the feature information from the text and improve the accuracy of the text classification task.
Self-Guided Network for Fine-Grained Object Localization Using Weakly Supervised Learning

Weakly supervised object localization (WSOL) aims at pre-dicting the location of objects with image-level labels. Fine-grained WSOL task has its characteristic challenge compared with generic object localization. The structural information of the objects in fine-grained benchmark has little relevance to class. Previous WSOL works mainly focus on learning the most class discriminative parts recursively, leading to se-rious structural feature missing issue. In this paper, we pro-pose a self-guided network (SGN) which consists of two branch deep classification networks. It adopts a coarse-to-fine strategy to detect the structural information of the ob-ject. First, we devise a self-adaptive method (SAM) to de-tect the most body structure of the object by directly leveraging the feature recognition ability of the first classifier. Then, an object structure generation (OSG) method is proposed in the fine localization phase. OSG helps the second classifier to learn the boundary feature of the object with less back-ground noise. Extensive experiments on four well-known fine-grained benchmarks, including CUB, FGVC Aircraft, Stanford Dogs, and Stanford Cars show that the proposed SGN outperforms the state-of-the-art WSOL methods.
Semantic-Related Feature Generation for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) is a challenging task. Although no visual samples of unseen classes are provided during training, the classifier must learn to recognize all classes (i.e. both seen and unseen classes). Due to the ability to generate unseen classes samples, generative models have been widely used in GZSL. However, these generative models only learn from the seen classes, so the discriminability of the unseen class features they generate is usually poor, resulting in low unseen class classification accuracy. To solve this problem, this paper proposes a novel semantic-related feature generative (SRFG) model to improve visual-semantic consistency and alleviate seen-unseen bias effectively. SRFG can generate any number of semantic-related discriminative features for both seen and unseen classes. Extensive experiments on four benchmark datasets show that the proposed model significantly outperforms the state of the arts.
G-Head: Gating Head for Multi-Task Learning in One-Stage Object Detection

Object detection is commonly formulated as a multi-task learning problem in deep learning methods. Due to the di-vergence between classification and regression tasks, modern one-stage detectors typically utilize two parallel branches as the detection head, which might be sub-optimal. In this paper, we propose a new Gating Head (G-Head) to enhance the in-teraction between different tasks and promote the multi-task learning process. By introducing Multi-Scale Aggregation (MSA), Multi-Aspect Learning (MAL), and Gating Selec-tor (GS), our method can significantly boost the performance of existing one-stage frameworks with fewer parameters and computational costs. To validate the efficiency, effectiveness, and generalization of our G- Head, extensive experiments are conducted on the challenging MS COCO dataset. Without bells and whistles, we achieve a new state-of-the-art 48.7 AP under single-model and single-scale test.
BACNet: Boundary-Anchor Complementary Network for Temporal Action Detection

The task of temporal action detection aims to locate and classify action segments in untrimmed videos. Most existing works usually consist of two components: snippet-level boundary segmentation and anchor-level action evaluation. These two components, however, are typically designed ir-relevantly, so the detection accuracy is undermined due to vague boundaries and complex video content. To tackle this problem, we design two supplementary modules. One mod-ule, termed as Anchor Aware Module (AAM), uses tem-poral and semantic related anchors to enhance snippet feature. The other module, named Boundary Aware Module (BAM), endows anchor feature with structured representation using intermediate supervision. Moreover, the ConvL-STM is applied to establish temporal relation in BAM with the structured representation. These two modules are in-tegrated as the Boundary-Anchor Complementary Network (BACNet), which achieves the state-of-the-art performance on both THUMOS-14 and ActivityNet-1.3 datasets.