Currently, people are actively seeking new and clean energy sources to replace traditional fossil fuels. As a widely distributed, abundant, and easily accessible renewable energy source, wind energy has multiple applications both domestically and internationally, and has a promising future. In wind power forecasting, this paper proposes a new algorithm LFTformer that combines the Transformer model with linear fuzzy information granulation (LFIG).The model uses the improved LFIG algorithm to extract the semantic information of time series, and divides the original time series into multiple information granules, which are then used as inputs to the Transformer model. Through comparative experiments for wind power prediction over 72 hours, 120 hours, and 168 hours, this research demonstrates that the LFTformer can improve the accuracy of wind power prediction. Finally, a Digital Twins system for wind power prediction is constructed using three-dimensional visualization techniques, displaying the prediction results and key data indicators, providing reliable data support for wind power systems.
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Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement
PDF ↗Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality data association problem for further heterogeneous joint learning. To address this issue, we propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations. Besides, we further propose a cross-modality neighbor consistency guided label refinement and regularization module, to eliminate the negative effects brought by the inaccurate supervised signals, under the assumption that the prediction or label distribution of each example should be similar to its nearest neighbors'. Extensive experimental results on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art approach by a large margin of 7.76% mAP on average, which even surpasses some supervised VI-ReID methods.
The rapid progress of the DeepFake technique has caused severe privacy problems. Thus protecting facial data against DeepFake becomes an urgent requirement. Face protection can be regarded as a bidirectional process: Face-out-detection (FOD) and Face-in-forensics (FIF). For FOD, the detectability should be satisfied when using the protected face to replace other faces. For FIF, traceability should be guaranteed when the protected face is replaced by others. For this, we propose a Bidirectional Facial-data Protection Framework (BiFPro) to protect face data comprehensively. This framework is composed of three main parts: Watermarking embedding, Face-out-detection (FOD) and Face-in-forensics (FIF). For the FOD case, we ensure the vulnerability of the original face by embedding fragile watermarking. Once the protected facial image is used to replace other faces, the watermarking information will be corrupted in the synthesized face images which can be used to detect the authenticity of the protected facial images. As for the FIF case, we guarantee the traceability of the protected face image by embedding robust watermarking, with which the fake faces can be traced with the reserved watermarking even after the face is swapped. Experimental results demonstrate that our proposed BiFPro could generate the watermarking which is fragile to FOD and at the same time robust to FIF with an average watermark extraction success rate reaching more than 95% when defending against the four advanced DeepFake techniques. Finally, we hope this work can encourage more initiative countermeasures against DeepFake.
Single image deraining aims to remove rain perturbation while restoring the clean background scene from a rain image. However, existing methods tend to produce blurry and over-smooth outputs, lacking some textural details. Wavelet transform can depict the contextual and textural information of an image at different levels, showing impressive capability of learning structural information in the images to avoid artifacts, and thus has been recently explored to consider the inherent overlap of background and rain perturbation in both the pixel domain and the frequency embedding space. However, the existing wavelet-based methods ignore the heterogeneous degradation for different coefficients due to the inherent directional characteristics of rain streaks, leading to inter-frequency conflicts and compromised deraining results. To address this issue, we propose a novel Direction-aware Attention Wavelet Network (DAWN) for rain streaks removal. DAWN has several key distinctions from existing wavelet transform-based methods: 1) introducing the vector decomposition to parameterize the learning procedure, where the rain streaks are derived into the vertical (V) and horizontal (H) components to learn the specific representation; 2) a novel direction-aware attention module (DAM) to fit the projection and transformation parameters to characterize the direction-specific rain components, which helps accurate texture restoration; 3) exploring practical composite constraints on the structure, details, and chrominance aspects for high-quality background restoration. Our proposed DAWN delivers significant performance gains on nine datasets across image deraining and object detection tasks, exceeding the state-of-the-art method MPRNet by 0.88 dB in PSNR on the Test1200 dataset with only 35.5% computation cost.
Voice-face association is generally specialized as a cross-modal cognitive matching problem, and recent attention has been paid on the feasibility of devising the computational mechanisms for recognizing such associations. Existing works are commonly resorting to the combination of contrastive learning and classification-based loss to correlate the heterogeneous datas. Nevertheless, the reliance on typical features of each category, known as archetypes, derived from the combination suffer from the weak invariance of modality-specific features within the same identity, which might induce a cross-modal joint feature space with calibration deviations. To tackle these problems, this paper presents an efficient Archetype-agnostic framework for reliable voice-face association. First, an Archetype-agnostic Subspace Merging (AaSM) method is carefully designed to perform feature calibration which can well get rid of the archetype dependence to facilitate the mutual perception of datas. Further, an efficient Bilateral Connection Re-gauging scheme is proposed to quantitatively screen and calibrate the biased datas, namely loose pairs that deviate from joint feature space. Besides, an Instance Equilibrium strategy is dynamically derived to optimize the training process on loose data pairs and significantly improve the data utilization. Through the joint exploitation of the above, the proposed framework can well associate the voice-face data to benefit various kinds of cross-modal cognitive tasks. Extensive experiments verify the superiorities of the proposed voice-face association framework and show its competitive performances with the state-of-the-arts.
Personalized fashion matching task aims to predict the compatible fashion items given available ones for specific users through the effective modeling of the third-order interaction patterns among the user and item pairs. To achieve this, previous methods separately model two key components, user-item and item-item relationships, which ignore the inherent correlations between them and lead to undesirable performance. With a new perspective, this paper proposes to formulate the personalized item matching as the multi-relational connectivity and apply a single-component translation operation to model the targeted third-order interactions. With user-item-item interactions naturally constructing a multi-relational graph, we further device two graph learning modules to enhance the translation-based matching approach from two perspectives,C ontext and Path. The proposed method, named CP-TransMatch, has been tested with extensive experiments on three benchmark fashion datasets and proven effective. It sets the new SOTA for the personalized fashion matching task.
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pretrained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pretrained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pretrained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU.
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.
The bottleneck of visual domain adaptation always lies in the learning of domain invariant representations. In this paper, we present a simple but effective technique named Adaptive Feature Swapping for learning domain invariant features in Unsupervised Domain Adaptation (UDA). Adaptive Feature Swapping aims to select semantically irrelevant features from labeled source data and unlabeled target data and swap these features with each other. Then the merged representations are also utilized for training with prediction consistency constraints. In this way, the model is encouraged to learn representations that are robust to domain-specific information. We develop two swapping strategies including channel swapping and spatial swapping. The former encourages the model to squeeze redundancy out of features and pay more attention to semantic information. The latter motivates the model to be robust to the background and focus on objects. We conduct experiments on object recognition and semantic segmentation in UDA setting and the results show that Adaptive Feature Swapping can promote various existing UDA methods. Our codes are publicly available at https://github.com/junbaoZHUO/AFS.
In this paper, we address the problem of simultaneous relighting and novel view synthesis of a complex scene from multi-view images with a limited number of light sources. We propose an analysis-synthesis approach called Relit-NeuLF. Following the recent neural 4D light field network (NeuLF)[22], Relit-NeuLF first leverages a two-plane light field representation to parameterize each ray in a 4D coordinate system, enabling efficient learning and inference. Then, we recover the spatially-varying bidirectional reflectance distribution function (SVBRDF) of a 3D scene in a self-supervised manner. A DecomposeNet learns to map each ray to its SVBRDF components: albedo, normal, and roughness. Based on the decomposed BRDF components and conditioning light directions, a RenderNet learns to synthesize the color of the ray. To self-supervise the SVBRDF decomposition, we encourage the predicted ray color to be close to the physically-based rendering result using the microfacet model. Comprehensive experiments demonstrate that the proposed method is efficient and effective on both synthetic data and real-world human face data, and outperforms the state-of-the-art results.
We present "Double Doodles'' to make full use of two sequential inputs of a VR controller with 9 DOFs in total, 3 DOFs of the first input sequence for the generation of motion paths and 6 DOFs of the second input sequence for motion gestures. While engineering our system, we take ergonomics into consideration and design a set of user-defined motion gestures to describe character motions. We employ a real-time deep learning-based approach for highly accurate motion gesture classification. We then integrate our approach into a prototype system, and it allows users to directly create character animations in VR environments using motion gestures with a VR controller, followed by animation preview and animation interactive editing. Finally, we evaluate the feasibility and effectiveness of our system through a user study, demonstrating the usefulness of our system for visual storytelling dedicated to amateurs, as well as for providing fast drafting tools for artists.
The increasing virtualization of the performance process has resulted in passive interactions between performer and audience based on the observing paradigm, but without the presence of in-person staging. We designed a more interactive paradigm of remote performance using a multimedia exhibition strategy where visitors can alter the environment of the performer's location by changing its music and lighting, whereas the performer can create engagement in the exhibition space by affecting a remote robotic arm. We conducted a case study with five participating performers, investigating their expectations, workflows, and perceptions before and after the performance-exhibition, and examining the video footage of their interactions with visitors to understand how they adapt and respond to this remote performance paradigm. We found that the robot arm was perceived as a mediating character with its own distinct identity, that musical changes have implicit behavioral effects on the performance, that lighting manipulations actively changed performer actions during improvisation, and that audiences appear to identify themselves and the robot as integral co-performers in this setup. This work provides insights into how performers learn to engage with audiences in novel distanced spaces and diverse interactive media, generating insights for future virtualized performative interactions beyond the classical stage metaphor.
With the recent advancements in multimedia and computer graphics technology, virtual avatar concerts have become increasingly popular among both well-known celebrities and subculture-based virtual YouTubers, attracting large audiences. Although this new form of performance art can be delivered through various platforms that use different viewing formats, research on the components of enjoying virtual avatar concerts and their effects on the audience experience is lacking. This study aims to explore the effects of different viewing formats and song genres on sickness, presence, and immersion and identify preferences for enjoying virtual avatar concerts. We first analyzed data from 64 virtual avatar concert cases to identify the most commonly used viewing media, methods, and song genres. Based on our analysis, we designed and conducted a user experiment with four virtual avatar concert scenarios. The experiment compared two viewing methods (audience's point of view and director's edited view) and two song genres (dance and ballad) for 2D display and a head-mounted display. The results show that the audience's point of view and a dance song can be advantageous in increasing the immersive experience irrespective of the viewing medium. Furthermore, the participants show different expectations regarding performances according to song genres. We discuss these findings and propose guidelines to help design and conduct virtual avatar concerts effectively.
Image animation involves generating a video of a source image imitating the pose of a driving video. Despite recent advancements in the image animation task, most state-of-the-art methods remain vulnerable to large pose changes. In cases of large pose changes, existing methods struggle to model the complex nonlinear motion and yield distorted results, which greatly restricts their application in the real world. To tackle this problem, we present a novel approach called Chain-of-Pose (CoP) that decomposes large pose changes into a sequence of intermediate pose changes. This enables us to handle simplified pose changes and improves the accuracy of pose estimation. Furthermore, to better preserve the appearance of the source object, we introduce the Appearance Refinement Module (ARM) that effectively integrates the appearance texture feature of the source image with the structural pose feature from the pose chain. Our experimental results demonstrate that our method qualitatively and quantitatively outperforms state-of-the-art approaches on four diverse datasets, comprising talking faces, human bodies, and pixel animals. Notably, our approach significantly improves video quality in the case of large object pose changes. Our code is attached to the supplementary material.
Current face sketch-photo synthesis researches generally embrace an image-to-image (I2I) translation pipeline. However, these methods ignore the one-to-many mapping problem (i.e., multiple plausible photo results can correspond to a single input sketch) in sketch-to-photo synthesis task, resulting in significant performance degradation on diverse datasets. Besides, generating high-quality images on limited data is also a challenge for this task. To address these challenges, we propose a dual-path framework that introduces generative priors to better perform cross-domain reconstruction on limited data. The coarse path uses a layer-swapped pre-trained generator to achieve coarse cross-domain reconstruction, and the refinement path further improves the structure and texture details. To align the feature maps between the two paths, we introduce a spatial feature calibration module. Despite this, our framework still struggles to handle diverse datasets. Thanks to the flexibility of generative priors, we can extend the framework to achieve exemplar-guided I2I translation by incorporating an exemplar with style mixing and a proposed semantic-aware style refinement strategy, which addresses the one-to-many mapping problem in sketch-to-photo synthesis task. Furthermore, our framework can perform cross-domain editing by employing off-the-shelf editing methods based on the latent space, achieving fine-grained control. Extensive experiments on diverse datasets demonstrate the superiority of our framework over other state-of-the-art methods.
3D skeleton is an inherent structure of objects and is often used for shape analysis. However, most supervised deep learning methods, which directly obtain 3D skeletons from 2D images, are constrained by skeleton data preparation. In this paper, we introduce a self-supervised method S3DS: a differentiable rendering-based method to reconstruct a 3D skeleton of shape from its single-view images, by using medial axis transformation (MAT) as its 3D skeleton. We use medial spheres (center positions and radii) to represent the 3D skeleton and use the connectivity of the spheres (medial mesh) to represent the topology. We trained a medial sphere prediction network, which reconstructs 3D skeleton spheres (centers and radii) from a single-view image and renders them into a 2D silhouette with many circles. Because of the radius, the center of the circle will fall on the 2D skeleton. Then the 3D spheres are fitted to the 3D skeleton by fitting many 2D circles onto the 2D skeleton. A mechanism is proposed to generate the connectivity of the discrete medial spheres and construct the 3D topology of the shape. We have conducted extensive experiments on public datasets and proved that S3DS has better performance than baseline and competitive performances with supervised methods on 3D skeletons reconstruction.
Computational aesthetic evaluation has made remarkable contribution to visual art works, but its application to music is still rare. Currently, subjective evaluation is still the most effective form of evaluating artistic works. However, subjective evaluation of artistic works will consume a lot of human and material resources. The popular AI generated content (AIGC) tasks nowadays have flooded all industries, and music is no exception. While compared to music produced by humans, AI generated music still sounds mechanical, monotonous, and lacks aesthetic appeal. Due to the lack of music datasets with rating annotations, we have to choose traditional aesthetic equations to objectively measure the beauty of music. In order to improve the quality of AI music generation and further guide computer music production, synthesis, recommendation and other tasks, we use Birkhoff's aesthetic measure to design a aesthetic model, objectively measuring the aesthetic beauty of music, and form a recommendation list according to the aesthetic feeling of music. Experiments show that our objective aesthetic model and recommendation method are effective.
Neural style transfer (NST) has attracted many research interests recent years. The existing NST schemes could only generate one stylized image from a content-style image pair. They are weak in creating diverse and personalized artistic styles. On the other hand, the stylized images could easily be stolen and illegally redistributed when shared online, which has not been addressed at all in the existing NST schemes. In this paper, we propose a personalized and watermark-guided style transfer network (PWST-Net) to tackle the aforementioned issues. Our PWST-Net could generate diverse stylized images from a content-style image pair using different personalization keys. Once the style transfer is done, our stylized images are with watermarks naturally embedded for copyright protection. We propose a novel style encoder in our PWST-Net to progressively generate the stylized images, which contains a Guided Fusion (GF) block and a Style Transformation (ST) block. The GF block generates a coarse stylized image based on a personalized direction field that is specific to a personalization key and the style image. The ST block refines the coarse stylized image into the final stylized image. It embeds a watermark into the deep feature space of the stylized image during the style transfer. To make the stylized images more diverse, we further propose a new personalization loss for training our PWST-Net. Various experiments demonstrate the effectiveness of our proposed method for generating personalized and watermarked stylized images, which also outperforms the state-of-the-art NST schemes in terms of artistic visual appearance.
Human Mesh Reconstruction (HMR) aims to recover 3D human poses and shapes from a single image. Existing parameterized HMR approaches follow the "representation-to-reasoning'' paradigm to predict human body and pose parameters. This paradigm typically involves intermediate representation and complex pipeline, where potential side effects may occur that could hinder performance. In contrast, query-based non-parameterized methods directly output 3D joints and mesh vertices, but they rely on excessive queries for prediction, leading to low efficiency and robustness. In this work, we propose a simple query-based framework, dubbed SimHMR, for parameterized human mesh reconstruction. This framework streamlines the prediction process by using a few parameterized queries, which effectively removes the need for hand-crafted intermediate representation and reasoning pipeline. Different from query-based non-parameterized HMR that uses excessive coordinate queries, SimHMR only requires a few semantic queries, which physically correspond to pose, shape, and camera. The use of semantic queries significantly improves the efficiency and robustness in extreme scenarios, e.g., occlusions. Without bells and whistles, øurs achieves state-of-the-art performance on 3DPW and Human3.6M benchmarks, and surpasses existing methods on challenging 3DPW-OCC. Code available at https://github.com/inso-13/SimHMR github.com/inso-13/SimHMR
In this paper, we propose a novel Semantics and Structure-aware face Swapping framework (S2Swap) that exploits semantics disentanglement and structure enhancement for high fidelity face generation. Different from previous methods that either 1) suffer from degraded generation fidelity due to insufficient identity-attributes disentanglement or 2) neglect the importance of structure information for identity consistency, our approach can achieve local facial semantics disentanglement beyond global identity while boosting identity consistency through structure enhancement. Specifically, to achieve identity-attributes disentanglement, our S2Swap is designed from global-local perspectives. Firstly, an Oriented Identity Transfer module is proposed to globally disentangle target identity and attributes under global identity semantics prior. Such global disentanglement enables source identity transfer to the individual target identity. Secondly, a Local Semantics Disentanglement module is devised to disentangle local identity and identity-irrelevant facial semantics, providing local semantic compensation for the global counterpart. Moreover, to boost identity consistency, a Structure-Aware Head Modeling module is introduced to provide the desired face structure enhancement through an intuitive face sketch. Finally, considering the identity-attributes trade-off, we adaptively integrate semantics and structure information in a self-learning manner. Extensive experiments qualitatively and quantitatively show that our method outperforms SOTA face swapping methods in terms of both identity transfer and attribute preservation.