Reviewerly/exHarmony huggingface dataset:
Effect of laser generated microgrooves geometric parameters on the shear strength of CFRP-Aluminium alloy adhesive joints

The surface microstructure is one of the most important factors affecting the mechanical performance of adhesive bonding joint of CFRP materials. The aim of this paper mainly focused on the influence of laser generated microgrooves geometric parameters (groove depth, distances, and patterns) on the shearing strength of CFRP/aluminium alloy adhesive joints. An IR laser with a wavelength of 1064 nm was applied to pretreat the CFRP surface. Scanning electron microscopy (SEM) and confocal laser scanning microscope (CLSM) were used to observe and measure the surface morphology, cross-sectional shape, groove parameters (depth, width) and surface roughness. The single-lap shear experiments were also conducted to exam the quality of the adhesive joint. Meanwhile, failure modes under different groove parameters were examined using a digital camera. The main parameters of the grooves and their interaction effects on the final joint strength were quantitatively analyzed by an interactive orthogonal experimental design. It was concluded that the interaction of groove pattern and groove distance was the most significant factor affecting shear strength, followed by groove pattern and groove distance.
Decentralized Autonomous Operations and Organizations in TransVerse: Federated Intelligence for Smart Mobility

Human and social factors are essential to transportation systems, yet top-down management fails to consider them sufficiently. Consequently, management strategies are not tailored to human needs and are inadequate in providing transportation intelligence. This article investigates a management architecture based on decentralized/distributed autonomous operations/organizations (DAOs) that considers both the technical and societal aspects in our transportation metaverse, TransVerse. This design maps people’s transportation needs in physical space to their digital counterparts in cyberspace, utilizing blockchain technology to guarantee the secure exchange of information and ultimately bring about the Internet of Minds (IoM). With the federated intelligence that emerged in IoM, we can devise reliable and prompt traffic decisions by incorporating consensus, community voting, and smart contracts into the organizational, coordination, and execution structure. Details on operational procedures and key technologies are also covered. To demonstrate the efficacy of DAOs-based management, a case study of world model-driven cooperative signal control is provided, indicating its promising application in future transportation management.
Deep Hash Distillation for Image Retrieval

In hash-based image retrieval systems, degraded or transformed inputs usually generate different codes from the original, deteriorating the retrieval accuracy. To mitigate this issue, data augmentation can be applied during training. However, even if augmented samples of an image are similar in real feature space, the quantization can scatter them far away in Hamming space. This results in representation discrepancies that can impede training and degrade performance. In this work, we propose a novel self-distilled hashing scheme to minimize the discrepancy while exploiting the potential of augmented data. By transferring the hash knowledge of the weakly-transformed samples to the strong ones, we make the hash code insensitive to various transformations. We also introduce hash proxy-based similarity learning and binary cross entropy-based quantization loss to provide fine quality hash codes. Ultimately, we construct a deep hashing framework that not only improves the existing deep hashing approaches, but also achieves the state-of-the-art retrieval results. Extensive experiments are conducted and confirm the effectiveness of our work. Code is at https://github.com/youngkyunJang/Deep-Hash-Distillation .
Self-Supervised SAR Despeckling Powered by Implicit Deep Denoiser Prior

Speckle removal is an important preprocessing step for synthetic aperture radar (SAR) imaging. Since speckle-free SAR images do not exist, supervised methods are not applicable. In this letter, we propose implicit deep denoiser prior (SAR-IDDP), a self-supervised method for SAR despeckling. SAR-IDDP uses a deep image prior (DIP) implicitly captured by the convolutional neural network (CNN) to formulate regularization instead of traditional hand-crafted priors. Specifically, we treat the output of the CNN as a “prior” that we denoise again after “renoising.” The CNN is updated to maximize the similarity between the again denoised image and its prior. The renoising procedure is designed based on the assumption of unit mean noise, while the spatial correlation of speckle is also involved. The despeckling ability of our method stems from CNN’s natural tendency to capture low-level image statistics. Experiments show that SAR-IDDP achieves significant improvements over existing model-based and self-supervised despeckling methods on both synthetic and real SAR images.
Harmonizer: Learning to Perform White-Box Image and Video Harmonization

Recent works on image harmonization solve the problem as a pixel-wise image translation task via large autoencoders. They have unsatisfactory performances and slow inference speeds when dealing with high-resolution images. In this work, we observe that adjusting the input arguments of basic image filters, e.g., brightness and contrast, is sufficient for humans to produce realistic images from the composite ones. Hence, we frame image harmonization as an image-level regression problem to learn the arguments of the filters that humans use for the task. We present a Harmonizer framework for image harmonization. Unlike prior methods that are based on black-box autoencoders, Harmonizer contains a neural network for filter argument prediction and several white-box filters (based on the predicted arguments) for image harmonization. We also introduce a cascade regressor and a dynamic loss strategy for Harmonizer to learn filter arguments more stably and precisely. Since our network only outputs image-level arguments and the filters we used are efficient, Harmonizer is much lighter and faster than existing methods. Comprehensive experiments demonstrate that Harmonizer surpasses existing methods notably, especially with high-resolution inputs. Finally, we apply Harmonizer to video harmonization, which achieves consistent results across frames and 56 fps at 1080P resolution.
Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification

Object categories are often grouped into a multi-granularity taxonomic hierarchy. Classifying objects at coarser-grained hierarchy requires global and common characteristics, while finer-grained hierarchy classification relies on local and discriminative features. Therefore, humans should also subconsciously focus on different object regions when classifying different hierarchies. This granularity-wise attention is confirmed by our collected human real-time gaze data on different hierarchy classifications. To leverage this mechanism, we propose a Cross-Hierarchical Region Feature (CHRF) learning framework. Specifically, we first design a region feature mining module that imitates humans to learn different granularity-wise attention regions with multi-grained classification tasks. To explore how human attention shifts from one hierarchy to another, we further present a cross-hierarchical orthogonal fusion module to enhance the region feature representation by blending the original feature and an orthogonal component extracted from adjacent hierarchies. Experiments on five hierarchical fine-grained datasets demonstrate the effectiveness of CHRF compared with the state-of-the-art methods. Ablation study and visualization results also consistently verify the advantages of our human attention-oriented modules. The code and dataset are available at https://github.com/visiondom/CHRF .
Globetrotter: Connecting Languages by Connecting Images

Machine translation between many languages at once is highly challenging, since training with ground truth re-quires supervision between all language pairs, which is dif-ficult to obtain. Our key insight is that, while languages may vary drastically, the underlying visual appearance of the world remains consistent. We introduce a method that uses visual observations to bridge the gap between languages, rather than relying on parallel corpora or topo-logical properties of the representations. We train a model that aligns segments of text from different languages if and only if the images associated with them are similar and each image in turn is well-aligned with its textual description. We train our model from scratch on a new dataset of text in over fifty languages with accompanying images. Experiments show that our method outperforms previous work on unsupervised word and sentence translation using retrieval. Code, models and data are available on globetrotter.cs.columbia.edu
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining

Deep visuomotor policy learning, which aims to map raw visual observation to action, achieves promising results in control tasks such as robotic manipulation and autonomous driving. However, it requires a huge number of online interactions with the training environment, which limits its real-world application. Compared to the popular unsupervised feature learning for visual recognition, feature pretraining for visuomotor control tasks is much less explored. In this work, we aim to pretrain policy representations for driving tasks by watching hours-long uncurated YouTube videos. Specifically, we train an inverse dynamic model with a small amount of labeled data and use it to predict action labels for all the YouTube video frames. A new contrastive policy pretraining method is then developed to learn action-conditioned features from the video frames with pseudo action labels. Experiments show that the resulting action-conditioned features obtain substantial improvements for the downstream reinforcement learning and imitation learning tasks, outperforming the weights pretrained from previous unsupervised learning methods and ImageNet pretrained weight. Code, model weights, and data are available at: https://metadriverse.github.io/ACO/ .
EclipSE: Efficient Long-Range Video Retrieval Using Sight and Sound

We introduce an audiovisual method for long-range text-to-video retrieval. Unlike previous approaches designed for short video retrieval (e.g., 5–15 s in duration), our approach aims to retrieve minute-long videos that capture complex human actions. One challenge of standard video-only approaches is the large computational cost associated with processing hundreds of densely extracted frames from such long videos. To address this issue, we propose to replace parts of the video with compact audio cues that succinctly summarize dynamic audio events and are cheap to process. Our method, named EclipSE (Efficient CLIP with Sound Encoding), adapts the popular CLIP model to an audiovisual video setting, by adding a unified audiovisual transformer block that captures complementary cues from the video and audio streams. In addition to being $$2.92\times $$ faster and $$2.34\times $$ memory-efficient than long-range video-only approaches, our method also achieves better text-to-video retrieval accuracy on several diverse long-range video datasets such as ActivityNet, QVHighlights, YouCook2, DiDeMo, and Charades. Our code is available at https://github.com/GenjiB/ECLIPSE .
Multi-objective path planning for mobile robot with an improved artificial bee colony algorithm

Effective path planning (PP) is the basis of autonomous navigation for mobile robots. Since the PP is an NP-hard problem, intelligent optimization algorithms have become a popular option to solve this problem. As a classic evolutionary algorithm, the artificial bee colony (ABC) algorithm has been applied to solve numerous realistic optimization problems. In this study, we propose an improved artificial bee colony algorithm (IMO-ABC) to deal with the multi-objective PP problem for a mobile robot. Path length and path safety were optimized as two objectives. Considering the complexity of the multi-objective PP problem, a well-environment model and a path encoding method are designed to make solutions feasible. In addition, a hybrid initialization strategy is applied to generate efficient feasible solutions. Subsequently, path-shortening and path-crossing operators are developed and embedded in the IMO-ABC algorithm. Meanwhile, a variable neighborhood local search strategy and a global search strategy, which could enhance exploitation and exploration, respectively, are proposed. Finally, representative maps including a real environment map are employed for simulation tests. The effectiveness of the proposed strategies is verified through numerous comparisons and statistical analyses. Simulation results show that the proposed IMO-ABC yields better solutions with respect to hypervolume and set coverage metrics for the later decision-maker.
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution

Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this paper, we propose a framework of series-parallel lookup tables (SPLUT) to alleviate the above issues and achieve efficient image super-resolution. On the one hand, we cascade multiple lookup tables to enlarge the receptive field of each extracted feature vector. On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images. By doing so, the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table-based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no longer need interpolation methods which introduce redundant computations so that our method can achieve faster inference speed. Extensive experimental results on five popular benchmark datasets show that our method obtains superior SISR performance in a more efficient way. The code is available at https://github.com/zhjy2016/SPLUT .
Learning Cross-Video Neural Representations for High-Quality Frame Interpolation

This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors. We propose C ross-Video Ne u ral Re presentation (CURE) as the first video interpolation method based on neural fields (NF). NF refers to the recent class of methods for neural representation of complex 3D scenes that has seen widespread success and application across computer vision. CURE represents the video as a continuous function parameterized by a coordinate-based neural network, whose inputs are the spatiotemporal coordinates and outputs are the corresponding RGB values. CURE introduces a new architecture that conditions the neural network on the input frames for imposing space-time consistency in the synthesized video. This not only improves the final interpolation quality, but also enables CURE to learn a prior across multiple videos. Experimental evaluations show that CURE achieves the state-of-the-art performance on video interpolation on several benchmark datasets. (This work was supported by CCF-2043134.)
HVS-Based Perception-Driven No-Reference Omnidirectional Image Quality Assessment

Evaluating the quality of panoramic images has gradually become a hot research topic with the development of virtual reality (VR) technology. Therefore, a novel method is proposed to assess the quality of omnidirectional images without any reference information. Inspired by the characteristics of the human visual system (HVS) and visual attention mechanism, the proposed model is composed of the structure feature, statistical feature, and saliency feature to measure the panoramic image quality, in which structure information is expressed by combining the local Taylor series with the local binary pattern (LBP) operator, gradient-based statistical information of panoramic images are summarized comprehensively from three levels: the gradient measure, the relative gradient magnitude, and the relative gradient orientation, and the saliency detection by combining simple priors (SDSP)-based saliency information is extracted in this article to enrich perception feature of our model and improve the visibility of the saliency region in the omnidirectional image. Finally, according to the subjective scores provided and the above features, we use support vector regression (SVR) to predict the objective scores. The experiments indicate that our model has more substantial competitiveness and stability than other state-of-the-art methods on two reliable databases.
Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.
A Novel Model for Breast Cancer Detection and Classification

Breast cancer is a dreadful disease that affects women globally. The occurrences of masses in the breast region are the main cause of breast cancer development. It is important to detect breast cancer as early as possible as this might increase the survival rate. The existing research methodologies have the problems of increased computation complexity and low detection accuracy. To overcome such problems, this paper proposes an efficient breast cancer detection and classification system based on mammogram images. Initially, the mammogram images are preprocessed so unwanted regions and noise are removed and the contrast of the images is enhanced using Homo Morphic Adaptive Histogram Equalization (HMAHE). Then, the breast boundaries are identified with the use of the canny edge detector. After that, the pectoral muscles present in the images are detected and removed using the Global Pixel Intensity-based Thresholding (GPIT) method. Then, the tumors are identified and segmented by the Centroid-based Region Growing Segmentation (CRGS) algorithm. Next, the tumors are segmented and clustered and feature extraction is carried out from the clustered tumors. After that, the necessary features are selected by using the Chaotic Function-based Black Widow Optimization Algorithm (CBWOA). The selected features are utilized by the Convolutional Squared Deviation Neural Network Classifier (CSDNN) which classifies the tumors into six different categories. The proposed model effectively detects and classifies breast tumors and its efficiency is experimentally proved by comparison with the existing techniques.
UAV Path Planning Based on the Combination of A-star Algorithm and RRT-star Algorithm

In order for UAVs to perform tasks safely and efficiently, path planning for UAVs is required. The A-star algorithm can calculate the optimal path, but it needs to rasterize the map in advance and run for a long time. When the map changes, the A-star algorithm cannot quickly obtain a new path. This work proposes a UAV path planning system using the combination of the A-star algorithm and the RRT-star algorithm, which provides the optimal path for the UAV and can generate a temporary path in time when the pre-planned path cannot be used. This scheme can improve the adaptability of UAV in UAM scene.
Pose-Appearance Relational Modeling for Video Action Recognition

Recent studies of video action recognition can be classified into two categories: the appearance-based methods and the pose-based methods. The appearance-based methods generally cannot model temporal dynamics of large motion well by virtue of optical flow estimation, while the pose-based methods ignore the visual context information such as typical scenes and objects, which are also important cues for action understanding. In this paper, we tackle these problems by proposing a Pose-Appearance Relational Network (PARNet), which models the correlation between human pose and image appearance, and combines the benefits of these two modalities to improve the robustness towards unconstrained real-world videos. There are three network streams in our model, namely pose stream, appearance stream and relation stream. For the pose stream, a Temporal Multi-Pose RNN module is constructed to obtain the dynamic representations through temporal modeling of 2D poses. For the appearance stream, a Spatial Appearance CNN module is employed to extract the global appearance representation of the video sequence. For the relation stream, a Pose-Aware RNN module is built to connect pose and appearance streams by modeling action-sensitive visual context information. Through jointly optimizing the three modules, PARNet achieves superior performances compared with the state-of-the-arts on both the pose-complete datasets (KTH, Penn-Action, UCF11) and the challenging pose-incomplete datasets (UCF101, HMDB51, JHMDB), demonstrating its robustness towards complex environments and noisy skeletons. Its effectiveness on NTU-RGBD dataset is also validated even compared with 3D skeleton-based methods. Furthermore, an appearance-enhanced PARNet equipped with a RGB-based I3D stream is proposed, which outperforms the Kinetics pre-trained competitors on UCF101 and HMDB51. The better experimental results verify the potentials of our framework by integrating various modules.