Reviewerly/exHarmony huggingface dataset:
Semantic-Guided Feature Distillation for Multimodal Recommendation

Multimodal recommendation exploits the rich multimodal information associated with users or items to enhance the representation learning for better performance. In these methods, end-to-end feature extractors (e.g., shallow/deep neural networks) are often adopted to tailor the generic multimodal features that are extracted from raw data by pre-trained models for recommendation. However, compact extractors, such as shallow neural networks, may find it challenging to extract effective information from complex and high-dimensional generic modality features. Conversely, DNN-based extractors may encounter the data sparsity problem in recommendation. To address this problem, we propose a novel model-agnostic approach called Semantic-guided Feature Distillation (SGFD), which employs a teacher-student framework to extract feature for multimodal recommendation. The teacher model first extracts rich modality features from the generic modality feature by considering both the semantic information of items and the complementary information of multiple modalities. SGFD then utilizes response-based and feature-based distillation loss to effectively transfer the knowledge encoded in the teacher model to the student model. To evaluate the effectiveness of our SGFD, we integrate SGFD into three backbone multimodal recommendation models. Extensive experiments on three public real-world datasets demonstrate that SGFD-enhanced models can achieve substantial improvement over their counterparts.
EERCA-ViT: Enhanced Effective Region and Context-Aware Vision Transformers for image sentiment analysis

Different parts of an image have a strong or weak guiding effect on emotions. The key to emotion recognition of images is to fully exploit the regions associated with emotions. Therefore, this paper proposes a visual sentiment classification model with two branches based on visual transformer, termed as Enhanced Effective Region and Context-Aware Vision Transformers (EERCA-ViT). This model includes a primary branch and an auxiliary branch. The primary branch simulates interdependencies between patches by squeezing and stimulating patches (P-SE), thereby highlighting effective region features in the global feature. The auxiliary branch removes feature patches that have been tagged by the primary branch through the context-aware module (CAM), forcing the network to discover different sentiment discriminative regions. At the same time, a two-part loss function is constructed to improve the robustness of the model. Finally, extensive experiments on six benchmark datasets show that the proposed method outperforms the state-of-the-art image sentiment analysis methods. Furthermore, the effectiveness of the different modules of the framework (P-SE and CAM) has been well demonstrated through extensive comparative experiments.
Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning

This paper introduces a new and challenging Hidden Intention Discovery (HID) task. Unlike existing intention recognition tasks, which are based on obvious visual representations to identify common intentions for normal behavior, HID focuses on discovering hidden intentions when humans try to hide their intentions for abnormal behavior. HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions. Fortunately, from a sociological and psychological perspective, we find that the difference between hidden and normal intentions can be reasoned from multiple micro-behaviors, such as gaze, attention, and facial expressions. Therefore, we first discover the relationship between micro-behavior and hidden intentions and use graph structure to reason about hidden intentions. To facilitate research in the field of HID, we also constructed a seminal dataset containing a hidden intention annotation of a typical theft scenario for HID. Extensive experiments show that the proposed network improves performance on the HID task by 9.9% over the state-of-the-art method SBP.
Weakly-Supervised Text Instance Segmentation

Text segmentation is a challenging computer vision task with many downstream applications. Current text segmentation models need to be trained with pixel-level annotations, which requires a lot of labor cost. In this paper, we take the first attempt to perform weakly-supervised text instance segmentation through bridging text recognition and text segmentation. We observe that text recognition models are able to produce the attention localization of each text instance. Based on this observation, we propose a two-stage Text Adaptive Refinement (TAR) module to generate the pseudo labels based on the attention map of a text recognizer. Meanwhile, we develop a text segmentation module to take the rough attention location as input to predict segmentation masks, which are supervised by the aforementioned pseudo labels. In addition, we introduce a mask-augmented contrastive learning by treating the segmentation result as an augmented version of the input text image, thus improving the visual representation and further enhancing the performance of both recognition and segmentation. The experimental results demonstrate that the proposed method outperforms the state-of-the-art (SOTA) weakly-supervised generic segmentation methods by 18.95% and 17.80% in fgIoU on ICDAR13-FST and TextSeg. On MLT-S, COCO-TS and Total-Text, the proposed method achieves about 82% of the fully-supervised methods' performance. When evaluated on instance segmentation, the proposed method exceeds existing SOTA methods by 23.32% and 21.34% on ICDAR13-FST and TextSeg, respectively. Code and Supplementary Materials are available at https://github.com/FudanVI/FudanOCR/tree/main/weakly-text-segmentation.
Deep learning video analytics for the assessment of street experiments: The case of Bologna

Planning infrastructures and services for sustainable urban mobility is one of the main challenges for European cities. Following these principles, in March 2022, Fondazione Innovazione Urbana and the Municipality of Bologna built a new public space for children in a dismissed parking area located near to a middle school, using the approach of tactical urban planning and participatory design. In this context, this study proposes a methodology for the integration of long term camera-based monitoring for the assessment of temporary streets experiments. The method differs from previous work from literature in its continuous and systematic approach, fostering the implementation of large scale quantitative methodologies in urban interventions. The area was the subject of an extended mobility study with the objective to monitor pedestrian and vehicular flows through video analytics techniques and to detail specific patterns of space use during the pre/ post-intervention phases (data collected over two months). The results of the analyses were processed in order to: (i) identify the activation times of the study area, through metrics describing cumulative pedestrian density and cumulative dwell time; (ii) characterize the relative uses of the study areas following the plaza redevelopment. Results revealed a 43 % growth in the cumulative dwell time recorded in the area following the redevelopment intervention. New use characteristics related to furniture in the plaza emerged, with the most significant cumulative dwell times increase being recorded around these spots. The results presented in this research have made it possible to quantify the effectiveness of the proposed urban regeneration intervention, confirming the use of sensors and innovative analysis technologies to support the iterative design process of urban regeneration interventions.
Sports Work Strategy of College Counselors Based on MySQL Database Big Data Analysis

In order to turn our attention to the real progress of the education world, this paper makes a concrete study on the efficiency optimization of MySQL database under big data. On the basis of discussing the significance of cultivating students' team spirit, this paper analyzes and discusses the strategies of cultivating students' team spirit in extracurricular sports activities. MySQL database, as one of the widely used open source databases, naturally has relatively excellent running efficiency, but it also poses a strong challenge for further improving the running efficiency of MySQL database. In many external services, databases are needed, whether in websites or other large-scale software. All the mobile users who need to use the database to store a large amount of user data have caused many websites to collect users and user behavior analysis, which can make them better optimize the website and products, so the dependence and application of data are increasing, and the optimization of databases can greatly improve the ability of developers to retrieve and analyze data.
Graph Convolutional Incomplete Multi-modal Hashing

Multi-modal hashing (MMH) encodes multi-modal data into latent hash code, and has been widely applied for efficient large-scale multi-modal retrieval. In practice it is common that multi-modal data is often corrupted with missing modalities, e.g., social image often lacks its tags in image-text retrieval. Conventional MMHs can only learn on complete modalities, which however wastes a considerable amount of collected data. To fulfill this gap, this paper proposes Graph Convolutional Incomplete Multi-modal Hashing (GCIMH) to learn hash code on incomplete multi-modal data. GCIMH develops Graph Convolutional Autoencoder to reconstruct incomplete multi-modal data with effective exploit of its semantic structure. GCIMH further develops multi-modal and label networks to encode multiple modalities and label respectively. GCIMH can successfully transfer knowledge of autoencoder and label network to multi-modal hashing network using teacher-student learning framework. GCIMH can handle missing modalities in both offline training and online query stages. Extensive empirical studies on three benchmark datasets demonstrate the superiority of the proposed GCIMH over the state-of-the-arts on both complete and incomplete multi-modal retrieval.
Deep Learning-Based Gender Classification by Training With Fake Data

Gender classification of human faces is a trending topic and a remarkable biometric task. This research area has useful applications in several fields, such as automated border control (ABC) and forensic work. There are many approaches to gender classification in the literature; the classical approaches usually use real faces. Although good performances have been achieved, data collection remains a problem. Additionally, the privacy of individuals must be included in many existing works. These drawbacks can be overcome by using fake faces. Recently, the creation of a robust fake face corpus using machine learning has become possible. Our main contribution in the present paper is to experimentally investigate the ability of an artificial deepfake corpus to be a substitute for real corpora in facial gender classification tasks. We propose a deep learning-based approach using convolutional neural networks trained with fake faces and tested on real faces. By exploiting artificial faces, data collection obstacles are resolved for the training step, and privacy is highly preserved. Four classifiers based on popular convolutional neural network architectures were implemented. In the test phase, we used faces of real identities extracted from well-known experimental databases such as Face Recognition Technology (FERET), Faculdade de Engenharia Industrial (FEI) faces, Face Recognition and Artificial Vision (FRAV) and Labeled Faces in the Wild (LFW). The results achieved are very promising. We obtained high accuracy rates and low EER scores. They are similar to those of research works using real faces. As a result of this work, we propose a gender-labeled deepfake facial dataset containing more than 200k deepfake corpora that we will make available upon request for research purposes.
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement

Recently, Users Generated Content (UGC) videos becomes ubiquitous in our daily lives. However, due to the limitations of photographic equipments and techniques, UGC videos often contain various degradations, in which one of the most visually unfavorable effects is the underexposure. Therefore, corresponding video enhancement algorithms such as Low-Light Video Enhancement (LLVE) have been proposed to deal with the specific degradation. However, different from video enhancement algorithms, almost all existing Video Quality Assessment (VQA) models are built generally rather than specifically, which measure the quality of a video from a comprehensive perspective. To the best of our knowledge, there is no VQA model specially designed for videos enhanced by LLVE algorithms. To this end, we first construct a Low-Light Video Enhancement Quality Assessment (LLVE-QA) dataset in which 254 original low-light videos are collected and then enhanced by leveraging 8 LLVE algorithms to obtain 2,060 videos in total. Moreover, we propose a quality assessment model specialized in LLVE, named Light-VQA. More concretely, since the brightness and noise have the most impact on low-light enhanced VQA, we handcraft corresponding features and integrate them with deep-learning-based semantic features as the overall spatial information. As for temporal information, in addition to deep-learning-based motion features, we also investigate the handcrafted brightness consistency among video frames, and the overall temporal information is their concatenation. Subsequently, spatial and temporal information is fused to obtain the quality-aware representation of a video. Extensive experimental results show that our Light-VQA achieves the best performance against the current State-Of-The-Art (SOTA) on LLVE-QA and public dataset. Dataset and Codes can be found at https://github.com/wenzhouyidu/Light-VQA.
Interactive Image Style Transfer Guided by Graffiti

Neural style transfer (NST) can quickly produce impressive artistic images, which allows ordinary people to become painter. The brushstrokes of stylized images created by the current NST methods are often unpredictable, which does not conform to the logic of the artist's drawing. At the same time, the style distribution of the generated stylized image texture differs from the real artwork. In this paper, we propose an interactive image style transfer network (IIST-Net) to overcome the above limitations. Our IIST-Net can generate stylized results for brushstrokes in arbitrary directions guided by graffiti curves. The style distribution of these stylized results is closer to the real-life artwork. Specifically, we design an Interactive Brush-texture Generation (IBG) module in IIST-Net to progressively generate controllable brush-textures. Then, two encoders are introduced to embed the interactive brush-textures into the content image in the deep space for producing the fused content feature map. The Multilayer Style Attention (MSA) module is proposed to further distill multi-scale style features and transfer them to the fused content feature map for obtaining the final stylized feature map with controllable brushstrokes. Additionally, we adopt the content loss, style loss, adversarial loss and contrastive loss to jointly supervise the proposed network. Experimental comparisons have demonstrated the effectiveness of our proposed method for creating controllable and realistic stylized images.
SemanticRT: A Large-Scale Dataset and Method for Robust Semantic Segmentation in Multispectral Images

Growing interests in multispectral semantic segmentation (MSS) have been witnessed in recent years, thanks to the unique advantages of combining RGB and thermal infrared images to tackle challenging scenarios with adverse conditions. However, unlike traditional RGB-only semantic segmentation, the lack of a large-scale MSS dataset has become a hindrance to the progress of this field. To address this issue, we introduce a SemanticRT dataset - the largest MSS dataset to date, comprising 11,371 high-quality, pixel-level annotated RGB-thermal image pairs. It is 7 times larger than the existing MFNet dataset, and covers a wide variety of challenging scenarios in adverse lighting conditions such as low-light and pitch black. Further, a novel Explicit Complement Modeling (ECM) framework is developed to extract modality-specific information, which is propagated through a robust cross-modal feature encoding and fusion process. Extensive experiments demonstrate the advantages of our approach and dataset over the existing counterparts. Our new dataset may also facilitate further development and evaluation of existing and new MSS algorithms.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains. The domain-invariant knowledge is transferred from the model trained on labeled source domain, e.g., video game, to unlabeled target domains, e.g., real-world scenarios, saving annotation expenses. Existing UDA methods for semantic segmentation usually focus on minimizing the inter-domain discrepancy of various levels, e.g., pixels, features, and predictions, for extracting domain-invariant knowledge. However, the primary intra-domain knowledge, such as context correlation inside an image, remains under-explored. In an attempt to fill this gap, we revisit the current pixel contrast in semantic segmentation and propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts. The proposed framework exploits the inherent structures of intra-domain images, which: (1) explicitly encourages learning the discriminative pixel-wise features with intra-class compactness and inter-class separability, and (2) motivates the robust feature learning of the identical patch against different contexts or fluctuations. Extensive experiments verify the effectiveness of the proposed method, which obtains competitive accuracy on the two widely-used UDA benchmarks, e.g., 75.6 mIoU on GTA→Cityscapes and 68.2 mIoU on Synthia→Cityscapes. Moreover, our method is compatible with other UDA approaches to further improve the performance without introducing extra parameters.
Towards a Formally Verified Security Monitor for VM-based Confidential Computing

Confidential computing is a key technology for isolating high-assurance applications from the large amounts of untrusted code typical in modern systems. Existing confidential computing systems cannot be certified for use in critical applications, like systems controlling critical infrastructure, hardware security modules, or aircraft, because they lack formal verification.
DecenterNet: Bottom-Up Human Pose Estimation Via Decentralized Pose Representation

Multi-person pose estimation in crowded scenes remains a very challenging task. This paper finds that most previous methods fail to estimate or group visible keypoints in crowded scenes rather than reasoning invisible keypoints. We thus categorize the crowded scenes into entanglement and occlusion based on the visibility of human parts and observe that entanglement is a significant problem in crowded scenes. With this observation, we propose DecenterNet, an end-to-end deep architecture to perform robust and efficient pose estimation in crowded scenes. Within DecenterNet, we introduce a decentralized pose representation that uses all visible keypoints as the root points to represent human poses, which is more robust in the entanglement area. We also propose a decoupled pose assessment mechanism, which introduces a location map to adaptively select optimal poses in the offset map. In addition, we have constructed a new dataset named SkatingPose, containing more entangled scenes. The proposed DecenterNet surpasses the best method on SkatingPose by 1.8 AP. Furthermore, DecenterNet obtains 71.2 AP and 71.4 AP on the COCO and CrowdPose datasets, respectively, demonstrating the superiority of our method. We will release our source code, trained models, and dataset to facilitate further studies in this research direction. Our code and dataset are available in https://github.com/InvertedForest/DecenterNet.
Striking a Balance: Unsupervised Cross-Domain Crowd Counting via Knowledge Diffusion

Supervised crowd counting relies on manual labeling, which is costly and time-consuming. This led to an increased interest in unsupervised methods. However, there is a significant domain gap issue in unsupervised methods, which is manifested by a model trained on one dataset serving dramatic performance drops when being transferred to another. This phenomenon can be attributed to the diverse domain knowledge making it difficult for the unsupervised models to transfer between general (e.g., similar distribution) and domain-specific (e.g., unique density, perspective, illumination, etc.) knowledge, leading to knowledge bias. Existing methods focus on exploring distinguishable relationships and establishing connections between the source and target domains. However, the similar knowledge transfer cannot perfectly simulate the contents of the target domain, leading to the model's inability to generalize to domain-specific knowledge. In this paper, we propose a Self-awareness Knowledge Diffusion method (SaKnD) that leverages the self-knowledge without establishing cross-domain knowledge relationships, which aims to balance the knowledge bias between general and domain-specific knowledge. Specifically, we propose a strategy to evaluate the uncertainty and consistency to define the clueless and informed areas, which determine the location and orientation of knowledge diffusion. These clueless areas serve as domain-specific knowledge that needs to be optimized, and these informed areas serve as general knowledge across domains. Extensive experiments on three standard crowd-counting benchmarks, ShanghaiTech PartA, ShanghaiTech PartB, and UCF_QNRF, show that the proposed SaKnD achieves state-of-the-art performance.
Modality-agnostic Augmented Multi-Collaboration Representation for Semi-supervised Heterogenous Face Recognition

Heterogeneous face recognition (HFR) aims to match input face identity across different image modalities. Due to the existing large modality gap and the limited number of training data, HFR is still a challenging problem in biometrics and draws more and more attention. Existing researchers always extract modality invariant features or generate homogeneous images to decrease the modality gap, lacking abundant labeled data to avoid the overfitting problem. In this paper, we proposed a novel Modality-Agnostic Augmented Multi-Collaboration representation for Heterogeneous Face Recognition (MAMCO-HFR) in a semi-supervised manner. The modality-agnostic augmentation strategy is proposed to generate adversarial perturbations to map unlabeled faces into the modality-agnostic domain. The multi-collaboration feature constraint is designed to mine the inherent relationships between diverse layers for discriminative representation. Experiments on several large-scale heterogeneous face datasets (CASIA NIR-VIS 2.0, LAMP-HQ and Tufts Face dataset) prove the proposed algorithm can achieve superior performance compared with state-of-the-art methods. The source code is available at https://github.com/xiyin11/Semi-HFR.