Reviewerly/exHarmony huggingface dataset:
Semantic-Information Space Sharing Interaction Network for Arbitrary Shape Text Detection

Arbitrary shape text detection is a challenging task due to significant variations in text shapes, sizes, and aspect ratios. Previous approaches relying on single-level feature map generated through a top-down fusion of different feature levels have limitations in harnessing high-level semantic information and expressing multi-scale features. To address these challenges, this paper introduces a novel arbitrary shape scene text detector called the Semantic-information Space Sharing Interaction Network (SSINet). The proposed network leverages the Semantic-information Space Sharing Module (SSM) to generate a single-level feature map capable of expressing multi-scale features with rich semantic and prominent foreground, enabling effective processing of text-related information. Experimental evaluations on three benchmark datasets, namely CTW-1500, MSRA-TD500, and ICDAR2017-MLT, validate the effectiveness of our method. The proposed SSINet achieves impressive results with an F-score of 86.0% on CTW-1500, 89.1% on MSRA-TD500, and 72.4% on ICDAR2017-MLT. The code will be available at https://github.com/123cjjjj/SSINet .
Industrial-SAM with Interactive Adapter

Image segmentation is a fundamental task in computer vision that aims to partition an image into multiple regions or objects. The recently proposed Segment Anything Model (SAM) is a powerful and promptable model for image segmentation that can produce high-quality object masks from input prompts such as points or boxes. However, recent studies have found that SAM, which is trained on natural images, performs poorly on non-natural images, such as industrial images, where the objects and backgrounds may have different characteristics and distributions. To address this problem, we propose Industrial-SAM, a SAM adapted for industrial vision applications. Industrial-SAM leverages the pre-trained SAM as a backbone and fine-tunes it on domain-specific datasets of industrial images. To efficiently train Industrial-SAM and make it suitable for industrial vision, we design an Interactive Adapter. Interactive Adapter is a novel module that can incorporate human feedback during training to guide the model learning. It can also generate adaptive prompts for different images based on their content and difficulty. We conduct extensive experiments on the cap welding segmentation dataset and seal pin welding segmentation dataset. The experimental results show that Industrial-SAM can train excellent industrial vision models with the help of human feedback.
Multi-feature Fusion-Based Central Similarity Deep Supervised Hashing

The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Nevertheless, previous deep supervised hashing methods merely focus on the high-level features of the image and neglect the low-level features of the image. Low-level features usually contain more detailed information. Therefore, we propose a multi-feature fusion-based central similarity deep supervised hashing method. Specifically, a cross-layer fusion module is designed to effectively fuse image features of high and low levels. On top of that, a channel attention module is introduced to filter out the useless information in the fused features. We perform comprehensive experiments on three widely-studied datasets: NUS-WIDE, MS-COCO and ImageNet. Experimental results indicate that our proposed method has superior performance compared to state-of-the-art deep supervised hashing methods.
Central and Directional Multi-neck Knowledge Distillation

There are already many mature methods for single-teacher knowledge distillation in object detection models, but the development of multi-teacher knowledge distillation methods has been slow due to the complexity of knowledge fusion among multiple teachers. In this paper, we point out that different teacher models have different detection capabilities for a certain category, and through experiments, we find that for individual target instances, there are differences in the main feature regions of the target, and the detection results also have randomness. Networks that are weak in overall detection performance for a certain category sometimes perform well, so it is not appropriate to simply select the best learning based on detection results. Therefore, we propose a novel multi-teacher distillation method that divides the central and boundary features of the instance target region through the detection of teacher models, and uses clustering to find the center of the response features of each category of teacher models as prior knowledge to guide the learning direction of the student model for the category as a whole. Since our method only needs to calculate the loss on the feature map, FGD can be applied to multiple teachers with the same components. We conducted experiments on various detectors with different backbones, and the results show that our student detector achieved excellent mAP improvement. Our distillation method achieved an mAP of 39.4% on COCO2017 based on the ResNet-50 backbone, which is higher than the single-teacher distillation learning method of the baseline model. Our code and training logs can be obtained at https://github.com/CCCCPRCV/Multi_ Neck .
Enhancing CLIP-Based Text-Person Retrieval by Leveraging Negative Samples

Text-person retrieval (TPR) is a fine-grained cross-modal retrieval task that aims to find matching person images through detailed text descriptions. Various recent cross-modal pre-trained vision-language based models (e.g., CLIP [12]) have greatly improved the performance of text-image retrieval (TIR), but direct migration of these general models to the TPR has limited effectiveness: CLIP encodes images and text into global features separately, which makes it difficult to focus on fine-grained attributes of person, and due to the limited volume of TPR, fine-tuning CLIP may cause unstable parameter updates. In this paper, we offer a cost-effective approach to improve CLIP-based models by constructing negative samples and learning knowledge from them. Specifically, 1) replacing specific types of attribute words in the original text to generate hard negative text that do not match the semantics of the image, improving the model’s understanding of fine-grained semantic knowledge; 2) a momentum contrastive learning framework for image intra-modal is introduced to scale up the data within a batch, thus providing enough negative samples and additional positive samples to learn person identity features, ensuring the gradient stability of the CLIP-based model. The proposed method achieves good results on three existing publicly available datasets.
A Graph-Involved Lightweight Semantic Segmentation Network

To extract cues for pixelwise segmentation in an efficient way, this paper proposes a lightweight model that involves graph structure in the convolutional network. First, a cross-layer module is designed to adaptively aggregate hierarchical features according to the feature relations within multi-scale receptive fields. Second, a graph-involved head is presented, capturing long-range channel and feature dependencies in two sub-domains. Specifically, channel dependency is acquired in a compact spatial domain for context-aware information, while the feature dependency is obtained in the graph feature domain for category-aware representation. Afterwards, by fusing the features with long-range dependencies, the network outputs the segmentation results after a learning-free upsampling layer. Experimental results present that this model remains light while achieving competitive performances in segmentation, proving the effectiveness and efficiency of the proposed sub-modules. ( https://github.com/xia-xx-cv/Graph-Lightweight-SemSeg/ ).
Deep Arbitrary-Scale Unfolding Network for Color-Guided Depth Map Super-Resolution

Although color-guided Depth map Super-Resolution (DSR) task has made great progress with the help of deep learning, this task still suffers from some issues: 1) many DSR networks are short of good interpretability; 2) most of the popular DSR methods cannot achieve arbitrary-scale up-sampling for practical applications; 3) dual-modality gaps between color image and depth map may give rise to texture-copying problem. As for these problems, we build a new joint optimization model for two tasks of high-low frequency decomposition and arbitrary-scale DSR. According to alternatively-iterative update formulas of the solution for these two tasks, the proposed model is unfolded as Deep Arbitrary-Scale Unfolding Network (DASU-Net). In the DASU-Net, we propose a Continuous Up-Sampling Fusion (CUSF) module to address two problems of arbitrary-scale feature up-sampling and dual-modality inconsistency during color-depth feature fusion. A large number of experiments have demonstrated that the proposed DASU-Net achieves more significant reconstruction results as compared with several state-of-the-art methods.
PiDiNeXt: An Efficient Edge Detector Based on Parallel Pixel Difference Networks

The Pixel Difference Network (PiDiNet) is well-known for its success in edge detection. Combining traditional operators with deep learning, PiDiNet achieves competitive results with fewer parameters. However, the complex and inefficient choice of traditional edge detection operators hinders PiDiNet’s further development. Therefore, we propose a novel lightweight edge detector called PiDiNeXt, which combines traditional edge detection operators with deep learning-based model in parallel to solve the operators choice problem and further enrich features. The results of experiments on BSDS500 and BIPED datasets demonstrate that PiDiNeXt outperforms PiDiNet in terms of accuracy. Moreover, we employ the reparameterization technique to prevent the extra computational cost caused by the multi-branch construction. This enables PiDiNeXt to achieve an inference speed of 80 FPS, comparable to that of PiDiNet. Furthermore, the lightweight version of PiDiNeXt can achieve an inference speed of over 200 FPS, meeting the needs of most real-time applications. The source code is available at https://github.com/Li-yachuan/PiDiNeXt .
Transpose and Mask: Simple and Effective Logit-Based Knowledge Distillation for Multi-attribute and Multi-label Classification

Knowledge distillation (KD) improves a student network by transferring knowledge from a teacher network. Although KD has been extensively studied in single-labeled image classification, it is not well explored under the scope of multi-attribute and multi-label classification. We observe that the logit-based KD method for the single-label scene utilizes information from multiple classes in a single sample, but we find such logits are less informative in the multi-label scene. To address this challenge in the multi-label scene, we design a Transpose method to extract information from multiple samples in a batch instead of a single sample. We further note that certain classes may lack positive samples in a batch, which can negatively impact the training process. To address this issue, we design another strategy, the Mask, to prevent the influence of negative samples. To conclude, we propose Transpose and Mask Knowledge Distillation (TM-KD), a simple and effective logit-based KD framework for multi-attribute and multi-label classification. The effectiveness of TM-KD is confirmed by experiments on multiple tasks and datasets, including pedestrian attribute recognition (PETA, PETA-zs, PA100k), clothing attribute recognition (Clothing Attributes Dataset), and multi-label classification (MS COCO), showing impressive and consistent performance gains.
Deep Face Recognition with Cosine Boundary Softmax Loss

To improve the accuracy of face recognition when there are wrong-labeled samples, a new deep face recognition model with cosine boundary loss is proposed in this paper. First, the proposed model uses the cosine similarity to determine the boundary that divides training samples into easy samples, semi-hard samples and harder samples, which play different roles during the training process. Then, an adaptive weighted piecewise loss function is developed to emphasize semi-hard samples and suppress wrong-labeled samples in harder samples by assigning different weights to related types of samples during different training stages. Compared with the state-of-the-art face recognition methods, i.e., CosFace, CurricularFace, and EnhanceFace, experimental results on CFP_FF, CFP_FP, AgeDB, LFW, CALFW, CPLFW, VGG2_FP datasets demonstrate that the proposed method can effectively reduce the impact of the wrong-labeled samples and provide a better accuracy.
DT-TransUNet: A Dual-Task Model for Deepfake Detection and Segmentation

The swift advancement of deepfake technology has given rise to concerns regarding its potential misuse, necessitating the detection of fake images and acquisition of supportive evidence. In response to this need, we present a novel dual-task network model called DT-TransUNet, which concurrently performs segmentation for both deepfake detection and deepfake segmentation. Additionally, we propose a new Multi-Scale Spatial Frequency Feature (MSSFF) module that employs the Stationary Wavelet Transform (SWT) to extract multi-scale high-frequency components and enhance these features using a texture activation function. When evaluated on multiple datasets, DT-TransUNet surpasses comparable methods in performance and visual segmentation quality, thereby validating the effectiveness and capability of the MSSFF module for deepfake detection and segmentation tasks.
AnoCSR–A Convolutional Sparse Reconstructive Noise-Robust Framework for Industrial Anomaly Detection

Industrial anomaly detection involves the identification and localization of abnormal regions in images, of which the core challenge is modeling normal data in appropriate ways. Inspired by dictionary learning, we propose a convolutional sparse reconstructive noise-robust framework, named AnoCSR. The proposed convolutional sparse encoding block (CSE-Block) in AnoCSR treats the convolutional dictionary as learnable parameters, where the convolutional kernels serve as the atoms. By training on normal samples, we optimize the parameters of the CSE-Block to extract optimal sparse codes. The CSE-Block stacks to form a convolutional sparse reconstructive network (CSR-Net) to progressively extract the sparse code and reconstruct the input image inversely. This enables effective reconstruction of normal samples while inadequately reconstructing abnormal regions, thereby facilitating anomaly detection and localization. The CSR-Net is parallelly connected with the downstream Localizer, forming an end-to-end framework. Our experimental results demonstrate that AnoCSR achieves performance comparable to state-of-the-art image reconstruction-based methods on the MVTecAD dataset. Moreover, considering that noise may affect the modeling of normal data and the discrimination of anomalies, we conduct the simulated noise resistance experiment. The result demonstrates that AnoCSR significantly outperforms other similar methods, indicating its strong noise robustness in addition to its outstanding performance.
Deep Hough Transform for Gaussian Semantic Box-Lines Alignment

Image correction and trimming for the digitization of paper-based records is a challenging task. Existing models and techniques for object detection are limited and cannot be applied directly. For example, non-directional bounding boxes cannot correct skewed images, paper-based scanned images with complex boundaries (torn pages and damaged edges) cannot locate obvious bounding boxes, resizing high-resolution image leads to accuracy errors, etc. To this end, in this paper, we proposed a Boundary Detection Network (BDNet) based on deep hough transform, which implements semantic boundary detection with geometrical restriction in a coarse-to-fine mean. The model is mainly divided into two stages: coarse location and refined adjustment. The former predicts the boundaries’ orientations and positions at the down-sampled image. The latter refines the boundary positions using the image patches sampled from the coarsely-located boundary in the original image. Among them, each stage contains two main modules: The Semantic Box-Lines (SBL) utilizes Gaussian heatmaps to capture extensive boundary semantic information, while the Deep Hough Alignment (DHA) efficiently extracts global line orientations to align semantic boundaries (box or lines). Detailed experiments and metric analysis verify that our proposed model is effective and feasible on the open-source datasets we collected, i.e., for scanned images with a resolution of $$ 2500\times 3500 $$ pixels, our method can accurately locate content boundaries and achieve 0.95 IoU accuracy.
Penalty-Aware Memory Loss for Deep Metric Learning

This paper focus on the deep metric learning, which has been widely used in various multimedia tasks. One popular solution is to learn a suitable distance metric by using triplet loss terms with sophisticated sampling strategies, However, existing schemes usually do not well consider global data distributions, which makes the learned metric suboptimal. In this paper, we address this problem by proposing a Penalty-Aware Memory Loss (PAML) that fully utilizes the expressive power of the combination of both global data distribution and local data distribution to learn a high-quality metric. In particular, we first introduce a memory bank to build the category prototype, that can capture the global geometric structure of data from the training data. The memory bank allows imposing a penalty regularizer during the training procedure without significantly increasing computational complexity. Subsequently, a new triplet loss with softmax is defined to learn a new metric space via the classical SGD optimizer. Experiments on four widely used benchmarks demonstrate that the proposed PAML outperforms state-of-the-art methods, which is effective and efficient to improve image-level deep metric learning.
Consequence of Resistance to Change on AI Readiness: Mediating–Moderating Role of Task-oriented Leadership and High-Performance Work System in the Hospitality Sector

In the modern era of the fourth industrial revolution, where collaborative systems are the real innovation, people are still not prepared for the results of the third revolution, namely, artificial intelligence readiness (AIR). Hence, people didn’t have enough time to adapt to the change. This paper aimed to explore the impact of resistance to change on artificial intelligence readiness and also explores the mediating role of task-oriented leadership, moderating role of high-performance work system. The study employs a quantitative research design and collects data of 450 working employees from hospitality sector of Pakistan. SmartPLS is used to analyze structural equation modeling (SEM) to through mixed-methods approach. The study findings are consistent with the proposed study hypotheses that resistance to change has positively impacts RI readiness and task-oriented leadership. Furthermore, the findings also elaborate the mediation of task-oriented leadership between latent constructs and moderating role of high-performance work system between resistance to change and task-oriented leadership. The study concluded that the hospitality sector should focus on developing task-oriented leadership and high-performance work systems to overcome resistance to change and increase its AI readiness.
Strengthen Interpersonal Communication in Improving Library Services for People with Disabilities

This study aims to strengthen interpersonal communication in improving library services for people with disabilities. This research is motivated by the neglect of people with disabilities when communicating their needs in library services. The method used in this study is a qualitative method, by conducting an in-depth case study approach. Data was obtained through online observation from websites that support data retrieval, in the form of books or journals available. The results showed that the training of librarians in interpersonal communication with users with disabilities should be implemented, librarians must be able to adapt to information and Communication Technology. Communicate interpersonally librarians must also pay attention to five important points, namely (1) Reliability, (2) Responsiveness, (3) Assurance, (4) Emphaty, (5) Tangible. Based on these results, interpersonal communication plays a role in improving library services for people with disabilities. The important role of interpersonal communication is because librarians can understand and help people with disabilities, and provide the same feelings that others get. And can minimize misunderstandings that will occur between librarians and users with disabilities.
RELATIONSHIP BETWEEN GOOGLE SEARCH VOLUME AND FLOOD EVENTS IN INDONESIA: A GOOGLE TRENDS ANALYSIS

The increasing number of floods every year brings a wide impact; the average loss caused by floods every year reaches 4.64T (Rp). Information about floods is very important to improve the preparedness of people living in disaster-prone areas. If information can be provided when people have a high interest in learning, it can have a profound effect on increasing public knowledge. To carry out effective risk communication in flood disasters, knowledge about flood disaster patterns is needed, as well as knowledge about community behavior in seeking information. One of the people's behavior in searching for information can be seen from the Google Trends analysis. The purposes of this study are to analyse the relationship between public interest in finding information through google trends with the keyword "flood" with flood events in Indonesia for 10 years. This research is a quantitative descriptive study by processing data obtained from Google Trends and BNPB flood event data using the Spearman Rank Correlation statistical test with SPSS software. The results showed that Google Trends with the keyword "Flood" and the pattern of flood events had a strong positive relationship with a correlation coefficient of 0.64, this indicates that the increase in the incidence of disasters causes an increase in public interest in seeking information about flood disasters. Risk communication that was carried out before the disaster occurred was not yet optimal in reaching the community. Risk communication needs to be carried out before the months that are predicted to be the peak of flood events. Risk communication is carried out to form the preparedness of the Indonesian people so that the risk of flood disasters can be reduced.
Disentangling Code-Switches from a Socio-functional Angle

Various functions of the code-switches within the present dataset are analyzed and a qualitative analysis of selected excerpts provided. More than half (54%) of the code-switches serve a discourse-related function whereas 36% are participant-related. A small set of code-switches is discussed for its potential connection to DAT.
The Formal Characteristics of the Bilingual Turns and Turn Boundaries

Focusing on the bilingual language material from all ten conversations of the dataset, bilingual turn boundaries and bilingual turns are analyzed both quantitatively and qualitatively. Moreover, turn-internal bilingual patterns are classified according to their structure.