Reviewerly/exHarmony huggingface dataset:
INTEGRATION OF SYNERGETIC AND SOCIO-CULTURAL APPROACHES IN SOCIAL AND HUMANITARIAN EDUCATION

The author analyses the integration of synergetic and socio-cultural approaches in social and humanitarian education. Such integration allows to consider the world of education, society, culture and man holistically, in their undivided unity, interconnection and mutual influence, as non-linearly self-developing and self-organizing open systems. The study aims to investigate heuristic possibilities of synergetic approach to study the dynamics of the development of the system of social and humanitarian education, exposed to the external impact of emerging transformations of the socio-cultural environment. The possibilities of implementing the principles of synergetics, namely, self-development, self-organization, complementarity, integrability, co-evolution of complex open human systems, which include the system of university social and humanitarian education are shown. Moreover, the limitations of using synergetic approach in social and humanitarian education in the changed conditions of the international situation and the expansion of modern Western culture are described as the changed conditions lead to the loss of traditional Russian values. The author proposed to supplement the synergetic approach with a socio-cultural approach to social and humanitarian education, focused on preserving the traditions and identity of national culture, on the formation of the civil identity of Russian society. The proposal is based on a new Concept of teaching social and humanitarian disciplines, which focuses on changing the value priorities of education. Pedagogical conditions for the implementation of mutually complementary synergetic and socio-cultural approaches are offered. The conditions aimed at creating an open socio-cultural educational space; at developing students' intercultural competence in the process of tolerant dialogue, and at organizing joint creative search to solve social problems based on the use of interactive teaching methods are offered.
RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization

Large ground-truth datasets and recent advances in deep learning techniques have been useful for layout detection. However, because of the restricted layout diversity of these datasets, training on them requires a sizable number of annotated instances, which is both expensive and time-consuming. As a result, differences between the source and target domains may significantly impact how well these models function. To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain. In this research, we introduced a synthetic document dataset called RanLayNet, enriched with automatically assigned labels denoting spatial positions, ranges, and types of layout elements. The primary aim of this endeavor is to develop a versatile dataset capable of training models with robustness and adaptability to diverse document formats. Through empirical experimentation, we demonstrate that a deep layout identification model trained on our dataset exhibits enhanced performance compared to a model trained solely on actual documents. Moreover, we conduct a comparative analysis by fine-tuning inference models using both PubLayNet and IIIT-AR-13K datasets on the Doclaynet dataset. Our findings emphasize that models enriched with our dataset are optimal for tasks such as achieving 0.398 and 0.588 mAP95 score in the scientific document domain for the TABLE class.
One-Epoch Training for Object Detection in Fisheye Images

This challenge is divided into two stages: qualification and final competition. We will acquire regular image data and need to perform detection on images with a fisheye effect. The approach described in this context begins by taking the original images and transforming them to mimic fisheye effect images for training. Furthermore, this challenge imposes limitations on computational resources, so striking a balance between accuracy and speed is a crucial aspect. In this paper, we asserted that our approach for this competition can achieve high performance with just one epoch of training. In summary, we achieved the top position among 24 participating teams in the qualification competition and secured the fourth position among the 11 successful submitted teams in the final competition. The corresponding source code will be available at: One-Epoch Training for Object Detection in Fisheye Images.
From Pixels to Explanations: Uncovering the Reasoning Process in Visual Question Answering

Visual reasoning requires models to construct a reasoning process towards the final decision. Previous studies have used attention maps or textual explanations to illustrate the reasoning process, but both have their limitations. Attention maps can be difficult to read, while textual explanations cannot fully describe the process of reasoning, and both are hard to evaluate quantitatively. This paper proposes a novel pixel-to-explanation reasoning model that employs a user-friendly multimodal rationale to depict the reasoning process. The model dissects the question into subquestions, and constructs reasoning cells to retrieve knowledge from the image and question based on these subquestions. The intermediate outcomes from the reasoning cells are translated into object bounding boxes and classes, with the final output beging classified as a standard VQA answer and translated into a complete answer to summarize the entire reasoning process. All the generated results can be combined to produce a human-readable and informative explanation that can be evaluated quantitatively. Besides the interpretability, we achieved a 4.4% improvement over our baseline model on the GQA dataset and attained new state-of-the-art results on the challenging GQA-OOD dataset.
TrackNetV3: Enhancing ShuttleCock Tracking with Augmentations and Trajectory Rectification

We present TrackNetV3, a sophisticated model designed to enhance the precision of shuttlecock localization in broadcast badminton videos. TrackNetV3 is composed of two core modules: trajectory prediction and rectification. The trajectory prediction module leverages an estimated background as auxiliary data to locate the shuttlecock in spite of the fluctuating visual interferences. This module also incorporates mixup data augmentation to formulate complex scenarios to strengthen the network’s robustness. Given that a shuttlecock can occasionally be obstructed, we create repair masks by analyzing the predicted trajectory, subsequently rectifying the path via inpainting. This process significantly enhances the accuracy of tracking and the completeness of the trajectory. Our experimental results illustrate a substantial enhancement over previous standard methods, increasing the accuracy from 87.72% to 97.51%. These results validate the effectiveness of TrackNetV3 in progressing shuttlecock tracking within the context of badminton matches. We release the source code at https://github.com/qaz812345/TrackNetV3.
TelEmoScatter: Enabling Remote Interaction and Emotional Connections in Virtual and Physical Music Performance

To enrich the emotional experiences of virtual reality (VR) online audiences in music performances, we developed TelEmoScatter, a system that facilitates remote interaction between music performers and onsite audiences. Our system also fosters emotional connections for online audiences through sound-visualization conversion, which is influenced by the state of the onsite audiences using computer vision techniques. In this work, we generate a 3D space using real-time sound-visualization techniques by converting MIDI signals from musical instruments into dynamic animations. Additionally, we employ video analysis to predict the emotions of the onsite audience, allowing seamless integration of emotional visual cues into the virtual scene. With our system, users can effortlessly immerse themselves in the emotional expressions of performers through music and experience the unique atmosphere of a live performance venue simply by wearing a VR headset.
Speech Spoofing Detection Based on Graph Attention Networks with Spectral and Temporal Information

Automatic speaker verification (ASV) systems are vulnerable to synthetic speech attacks. Synthetic algorithms usually introduce artifacts in specific sub-bands or time segments. However, under unknown spoofing attacks, it is challenging to choose the right domain for effective detection. In this paper, we propose a speech spoofing detection method based on graph attention networks with spectral and temporal information. First, high-level features of raw audio are extracted using SENet channel attention to enhance the spatial correlation between speech frames. Then, spectral graph and temporal graph are constructed for the high-level features using graph attention networks. Finally, we design a new heterogeneous multi-domain co-graph attention module to process the information from different domains for effective speech spoofing detection. The proposed model was evaluated on the ASVspoof 2019 dataset and obtains a min t-DCF of 0.0264 and an EER of 0.94%, exhibiting competitive performance. Experiments also show its effectiveness when detecting unknown types of attacks.
Learning a Robust Model with Pseudo Boundaries for Noisy Temporal Action Localization

Temporal Action Localization (TAL) aims to locate starting and ending times of actions and recognize categories in untrimmed videos. Significant progress has been made in developing deep models for TAL. The success of previous methods relies on large-scale training data with precise boundary annotations. However, fully accurate annotations are unpractical to be obtained due to the ambiguities of the action boundaries and the crowd-sourcing labeling process, leading to a degradation in performance. In this work, we take the first step into learning with inaccurate boundaries in TAL tasks. Motivated by the fact that inaccurate boundary annotations harm localization precision more than classification accuracy, we propose to use classification as a guidance signal to improve localization precision. Specifically, we introduce a pseudo-boundary generation and refinement method (PbGaR). PbGaR first treats each action segment as a bag of instances to select the instances with more accurate boundaries for training. Then these boundaries are refined via two strategies for higher quality. The proposed method significantly alleviates the degraded performance of TAL models under inaccurate boundaries. Extensive experiments on two popular datasets demonstrate the effectiveness of our method.
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization

Current speaker anonymization methods, especially with self-supervised learning (SSL) models, require massive computational resources when hiding speaker identity. This paper proposes an effective and parameter-efficient speaker anonymization method based on recent End-to-End model reprogramming technology. To improve the anonymization performance, we first extract speaker representation from large SSL models as the speaker identifies. To hide the speaker’s identity, we reprogram the speaker representation by adapting the speaker to a pseudo domain. Extensive experiments are carried out on the VoicePrivacy Challenge (VPC) 2022 datasets to demonstrate the effectiveness of our proposed parameter-efficient learning anonymization methods. Additionally, while achieving comparable performance with the VPC 2022 strong baseline 1.b, our approach also consumes less computational resources during anonymization.
A Decoupled Cross-layer Fusion Network with Bidirectional Guidance for Detecting Small Logos

Logo detection involves the use of machine learning algorithms to recognize and locate logos in images and videos, which has applications in a wide range of industries, including e-commerce, advertising, and entertainment. However, detecting small logos is still a challenging task due to their limited coverage of pixels and unclear details resulting in insufficient feature information for detection. Therefore, they are often easily confused by complex backgrounds and have lower perturbation tolerance to the bounding box, making them more difficult to detect compared to medium and large-scale logos. To address this problem, we propose a Decoupled Cross-layer Fusion Network (DCFNet) that enhances the feature representation of small logo objects, resulting in excellent detection performance. Specifically, the proposed DCFNet first adopts a bidirectional cross-layer connection mechanism to capture complementary information between different layers. Next, a two-phase feature averaging and enhancement strategy is used to further enhance the features. In the detection phase, DCFNet decouples the classification and boundary box regression branches into two identical Fully Connected (FC) heads, improving the accuracy of small logo classification and localization by avoiding mutual interference between the branches. Extensive experiments conducted on three publicly available logo datasets demonstrate that DCFNet achieves state-of-the-art performance in detecting small logos.
A Multi-scale and Dense Object Detector for Tibetan Thangka Images

Thangka cultural elements detection aims to locate and identify instances in Thangka. However, as a unique form of pictorial art, Thangka exhibits distinct spatial structures that deviate significantly from general images in scale and density. Therefore, it is challenging for most state-of-the-art detectors designed for natural scenes to handle Thangka cultural elements detection effectively. To overcome this issue, we propose a multi-scale and dense object detector referred as MDDet. It embeds a multi-scale receptive field fusion module (MRF) that enlarges the receptive field while capturing the spatial and channel relationships at different scales, which significantly enriches the multi-scale features extracted from the backbone. In addition, we introduce a threshold-slicing aided hyper inference (T-SAHI) scheme, which adaptively slices images in dense scenarios to aid with dense object detection in the test time. We thoroughly evaluate our method, and MDDet outperforms the prior art by a clear margin on the Thangka dataset, achieving an absolute improvement of 1.9% in average precision (AP). For the challenging medium and small objects in Thangka, MDDet obtains wide margins of 12% and 3.7% in accuracy improvement, respectively. It also shows strong generalization ability when evaluated on general scenarios, e.g., Pascal VOC 2007 and MS COCO, validating the role of MDDet in object detection.
Robust Tracking via Unifying Pretrain-Finetuning and Visual Prompt Tuning

The finetuning paradigm has been a widely used methodology for the supervised training of top-performing trackers. However, the finetuning paradigm faces one key issue: it is unclear how best to perform the finetuning method to adapt a pretrained model to tracking tasks while alleviating the catastrophic forgetting problem. To address this problem, we propose a novel partial finetuning paradigm for visual tracking via unifying pretrain-finetuning and visual prompt tuning (named UPVPT), which can not only efficiently learn knowledge from the tracking task but also reuse the prior knowledge learned by the pre-trained model for effectively handling various challenges in tracking task. Firstly, to maintain the pre-trained prior knowledge, we design a Prompt-style method to freeze some parameters of the pretrained network. Then, to learn knowledge from the tracking task, we update the parameters of the prompt and MLP layers. As a result, we cannot only retain useful prior knowledge of the pre-trained model by freezing the backbone network but also effectively learn target domain knowledge by updating the Prompt and MLP layer. Furthermore, the proposed UPVPT can easily be embedded into existing Transformer trackers (e.g., OSTracker and SwinTracker) by adding only a small number of model parameters (less than 1% of a Backbone network). Extensive experiments on five tracking benchmarks (i.e., UAV123, GOT-10k, LaSOT, TNL2K, and TrackingNet) demonstrate that the proposed UPVPT can improve the robustness and effectiveness of the model, especially in complex scenarios.
A Cross-modal and Redundancy-reduced Network for Weakly-Supervised Audio-Visual Violence Detection

Multimodal learning using audio and visual information has improved Violence Detection tasks. However, previous studies overlook the gap between pre-trained networks and the final violence detection task, as well as the semantic inconsistency between audio and visual features. We consider task-irrelevant information caused by the former situation and semantic noise due to the latter as redundancy, negatively affecting overall detection performance. Besides, the prevailing visual modality-centric approach with audio features as guidance may be biased. We contend that both modalities are crucial in violence detection. To address these issues, we propose a Cross-modal and Redundancy-reduced Network for Weakly-Supervised Audio-Visual Violence Detection. Our framework integrates a relation-ware module with a bi-directional cross-modal attention mechanism to explore interactions between modalities. Then, we introduce a feature filter gate to reduce redundancy. Finally, a multi-branch classification module is proposed for better utilization of both modalities. Extensive experiments demonstrate the effectiveness of our approach, surpassing previous methods with state-of-the-art performance in violence detection.
An Evaluation of Decentralized Group Formation Techniques for Flying Light Specks

Group formation is fundamental for 3D displays that use Flying Light Specks, FLSs, to illuminate shapes and provide haptic interactions. An FLS is a drone with light sources that illuminates a shape. Groups of G FLSs may implement reliability techniques to tolerate FLS failures, provide kinesthetic haptic feedback in response to a user's touch, and facilitate a divide and conquer approach to challenges such as localizing FLSs to render a shape. This paper evaluates four decentralized techniques to form groups. An FLS implements a technique autonomously using asynchronous communication and without a global clock. We evaluate these techniques using synthetic point clouds with known optimal solutions and real point clouds. Obtained results show a technique named Random Subset (RS) is superior when constructing small groups (G ≤ 5) while a different technique named Closest Available Neighbor First (CANF) is superior when constructing large groups (G ≥ 10).
GTTrack: Gaussian Transformer Tracker for Visual Tracking

Recently, Transformer based visual object tracking methods have achieved impressive advancements and significantly improved tracking performance. Transformer includes two modules of self-attention and cross-attention for those methods. However, it brings up two problems: first, the self-attention only considers the relative relation between elements when establishing global association, which can not highlight the essential areas of the tracked target. Second, the cross-attention only relies on feature similarity to locate the target, where the interference of similar objects is challenging. In this paper, we propose a new transformer tracking method of GTTrack by defining Gaussian Attention (GA) and Adaptive Focusing Module (AFM). The GA leads into Gaussian prior to generate a semantic template with robust object features, in which Gaussian prior pays more attention to the central region of the tracked target. The AFM calculates the similarity between current frame and the template by combining the appearance features and position features. The position features are defined with an adaptive Gaussian prior according to the target area in the previous frame. The introduction of position features enhances the contrast between the tracked target and the similar objects. Extensive experiments also demonstrate that the GTTrack outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.
A consulting system for guiding various image recognitions

In recent years, various image recognition tasks have been used in many real-world applications thanks to the development and open sources of computer vision technologies. However, the expertise of users is often required for selecting appropriate recognition engines for the analysis of given images. This limits the use of beginners who wanted to apply image recognitions for their real-world demands. To make such a selection process easier, we propose a consulting system in this paper that can automatically suggest appropriate recognition engines for given images or videos. In addition, the system can provide alternative editing operations, such as enlarging or shrinking, when the size or quality of an image is inappropriate for any recognitions. The effectiveness, easy-useness, and user-friendliness is demonstrated by the proposed consulting system.
Moving Inside the Box: Interacting with Interpretation of Historical Artefacts Through Tangible Augmented Reality

We present ARcheoBox, a walk-up-and-use prototype for interacting with interpretation of historical artefacts using tangible augmented reality. ARcheoBox enables users to manipulate virtual representations and interact with interpretation of historical artefacts using cylinder-shaped generic proxies. We also leverage the user interactions with interpretation using three interaction techniques “Move”, “Rotate”, and “Flip” as output modalities in AR. The prototype consists of a wooden box, a tablet display, and generic proxies, which means ARcheoBox does not require any head-mounted displays (HMDs), handheld controllers, or haptic gloves. We conducted a user study with 25 participants in which the findings demonstrate the advantages of tangible AR over more conventional interaction modalities presented in museums such as touch screens. Finally, we present a set of design recommendations for designing tangible AR that enhances the user's interaction experience with historical artefacts.
Object Detection via Fisheye Camera

During the competition, several factors that could decrease the effectiveness of the training result was quickly identified, such as the lack of distortion of provided training data, the high similarity between multiple images, and the extreme imbalance of quantity between classes. Due to the short duration of the competition, we proposed six simple-to-conduct yet proven very effective methods: 1) data filtering by skipping neighboring images, 2) data filtering to reduce high quantity classes, 3) use other datasets to replenish total quantity and abundancy, 4) develop an algorithm to distort a plain image accurately, 5) Pretrain on selected MS-COCO. Ablation studies were conducted to find out the effect of each part of the data. And found that using a selected set of MS-COCO to pre-train could increase its effect. Our work is straightforward in terms of methods used, but the result of our evaluation shows that it is reasonably solid.
Occlusion­-Aware Manga Character Re­-identification with Self-Paced Contrastive Learning

Existing methods for manga character re-identification primarily rely on facial information, overlooking the unique characteristics of characters’ bodies and failing to address common challenges like occlusion by speech balloons and incomplete body parts. To tackle these issues, we propose a method called Occlusion-Aware Manga Character Re-identification (OAM-ReID) with self-paced contrastive learning, which leverages annotated body data from the Manga109 dataset for training. By synthesizing data with occluded speech balloons and incomplete bodies, we empower the framework to be aware of occlusion, so that more effective feature representations are learnt. Experimental results show that this approach outperforms the state-of-the-art person ReID method.