Reviewerly/exHarmony huggingface dataset:
Enhance the Security of Access to IoT-based Equipment in Fog

The Internet of Things is in users’ daily life/work, as a result protecting privacy and security is essential. IoT’s heterogeneous, Pervasive, complicated properties make the problems of security important. Moreover, big nodes and resource limitations number make a lightweight need for algorithms of IoT security. So, attribute-based encryption (ABE) is the famous answer for the safe sharing, storage, and transfer of data in shared areas like IoT. In this paper, we propose a technique for user authentication with lightweight attribute-based encryption for IoTs in fog. With the security analysis performed, it is observed that the proposed technique prevents from the user attempting to decrypt messages that are not allowed, and thus, power dissipation and processing will be prevented for performing computational operations and processes that are spent with energy and time. Consequently, by performing this authentication before decoding, in addition to securing the security system, by removing the computational overheads required to achieve satisfaction at the requesting node, it adds the security technique performance used on the network. The results of the performance analysis show that overall, the proposed security framework has performed well.
Detecting Fraudulent Taxi Drivers: Overview

This review paper provides an overview of the techniques and approaches employed in the detection of fraudulent taxi drivers. By analyzing various studies and methodologies, the significance of addressing this issue in the transportation industry is highlighted. Different approaches, including anomaly detection techniques, machine learning algorithms, and network analysis, are discussed in the context of identifying suspicious patterns and behaviors exhibited by fraudulent taxi drivers. The utilization of large-scale transaction data, GPS data, and other relevant information is emphasized for effective fraud detection. The effectiveness of techniques such as clustering, classification, outlier detection, and feature selection in detecting anomalies and fraudulent activities in taxi services is explored. The integration of spatial, temporal, and cost factors is shown to enhance the accuracy and efficiency of fraud detection systems. However, challenges such as the evolving nature of fraudulent tactics, real-time detection requirements, and privacy concerns associated with data collection and analysis persist. Further research and development efforts are necessary to address these challenges and advance the field of detecting fraudulent taxi drivers. Overall, this paper provides a valuable overview of the techniques and approaches employed in the detection of fraudulent taxi drivers, enabling transportation authorities and service providers to proactively combat fraudulent activities, enhance passenger safety, and maintain the integrity of the taxi industry.
DeepFake Videos Detection by Using Recurrent Neural Network (RNN)

In the last few years, the increasing development of various tools to make fake videos from real videos has been raised. Thus, several models/approaches have been constructed to detect and reveals fake video. Consequently, this research is conducted to propose a new model based on combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and image preprocessing techniques to classify and find the fake video from the real video. To implement and evaluate the proposed model, a MATLAB simulator has been used. The deepFake Images dataset is used for evaluations. This dataset contains 135 real videos as well as 677 fake videos created using different tools on real videos. Two scenarios have been utilized to evaluate the performance of the proposed model which are; dimensions of the training data and different sizes of the training data. The results show that the proposed model has been able to provide better results than the previous model.
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by Li-DAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.3\times reduction$</tex> in model parameters and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64l\times fewer$</tex> multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).
CelebV-Text: A Large-Scale Facial Text-Video Dataset

Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents Celeb V- Text, a large-scale, di-verse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to- video generation tasks. CelebV-Text comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts gen-erated using the proposed semi-automatic text generation strategy. The provided texts are of high quality, describing both static and dynamic attributes precisely. The supe-riority of CelebV- Text over other datasets is demonstrated via comprehensive statistical analysis of the videos, texts, and text-video relevance. The effectiveness and potential of CelebV- Text are further shown through extensive self-evaluation. A benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task. All data and models are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page: https://celebv-text.github.io.
Mask3D: Pretraining 2D Vision Transformers by Learning Masked 3D Priors

Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to per-ceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pretraining to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pretraining approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.
DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields

Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs. Despite the impressive results, these methods cannot be applied to Generalizable NeRFs (GeNeRFs) which require image feature extractions that are often based on more complicated 3D CNN or transformer architectures. In this work, we first analyze the difficulties of jointly optimizing camera poses with GeNeRFs, and then further propose our DBARF to tackle these issues. Our DBARF which bundle adjusts camera poses by taking a cost feature map as an implicit cost function can be jointly trained with GeNeRFs in a self-supervised manner. Unlike BARF and its follow-up works, which can only be applied to per-scene optimized NeRFs and need accurate initial camera poses with the exception of forward-facing scenes, our method can generalize across scenes and does not require any good initialization. Experiments show the effectiveness and generalization ability of our DBARF when evaluated on real-world datasets. Our code is available at https://aibluefisher.github.io/DBARF.
Compacting Binary Neural Networks by Sparse Kernel Selection

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full code-book. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving

Vision-centric joint perception and prediction (PnP) has become an emerging trend in autonomous driving research. It predicts the future states of the traffic participants in the surrounding environment from raw RGB images. However, it is still a critical challenge to synchronize features obtained at multiple camera views and timestamps due to inevitable geometric distortions and further exploit those spatial-temporal features. To address this issue, we propose a temporal bird's-eye-view pyramid transformer (TBP-Former) for vision-centric PnP; which includes two novel designs. First, a pose-synchronized BEV encoder is proposed to map raw image inputs with any camera pose at any time to a shared and synchronized BEV space for better spatial-temporal synchronization. Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial priors. Extensive experiments on nuScenes dataset show that our proposed framework overall outperforms all state-of-the-art vision-based prediction methods. Code is available at: https://github.com/MediaBrain-SJTU/TBP-Former
SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates

Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE, a novel technique that improves NeRF reconstruction quality on sparse, unconstrained input views for in-the-wild indoor scenes. To constrain NeRF reconstruction, we leverage geometric priors in the form of per-view depth estimates produced with state-of-the-art monocular depth estimation models, which can generalize across scenes. A key challenge is that monocular depth estimation is an illposed problem, with inherent ambiguities. To handle this issue, we propose a new method that learns to predict, for each view, a continuous, multimodal distribution of depth estimates using conditional Implicit Maximum Likelihood Estimation (cIMLE). In order to disambiguate exploiting multiple views, we introduce an original space carving loss that guides the NeRF representation to fuse multiple hypothesized depth maps from each view and distill from them a common geometry that is consistent with all views. Experiments show that our approach enables higher fidelity novel view synthesis from sparse views. Our project page can be found at scade-spacecarving-nerfs.github.io.
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks

Deep neural networks (DNNs) are known to be vulnera-ble to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, in this paper we find an intriguing connection between them: for a model planted with backdoors, we observe that its adversarial examples have similar behaviors as its triggered images, i.e., both activate the same subset of DNN neurons. It indicates that planting a back-door into a model will significantly affect the model's adversarial examples. Based on these observations, a novel Progressive Backdoor Erasing (PBE) algorithm is proposed to progressively purify the infected model by leveraging un-targeted adversarial attacks. Different from previous back-door defense methods, one significant advantage of our approach is that it can erase backdoor even when the clean extra dataset is unavailable. We empirically show that, against 5 state-of-the-art backdoor attacks, our PBE can effectively erase the backdoor without obvious performance degradation on clean samples and outperforms existing de-fense methods.
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings

Distance-based classification is frequently used in transductive few-shot learning (FSL).However, due to the highdimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points.Hubness negatively impacts distance-based classification when hubs from one class appear often among the nearest neighbors of points from another class, degrading the classifier's performance.To address the hubness problem in FSL, we first prove that hubness can be eliminated by distributing representations uniformly on the hypersphere.We then propose two new approaches to embed representations on the hypersphere, which we prove optimize a tradeoff between uniformity and local similarity preservation -reducing hubness while retaining class structure.Our experiments show that the proposed methods reduce hubness, and significantly improves transductive FSL accuracy for a wide range of classifiers 1 .
WeatherStream: Light Transport Automation of Single Image Deweathering

Today single image deweathering is arguably more sensitive to the dataset type, rather than the model. We introduce WeatherStream, an automatic pipeline capturing all real-world weather effects (rain, snow, and rain fog degradations), along with their clean image pairs. Previous state-of-the-art methods that have attempted the all-weather removal task train on synthetic pairs, and are thus limited by the Sim2Real domain gap. Recent work has attempted to manually collect time multiplexed pairs, but the use of human labor limits the scale of such a dataset. We introduce a pipeline that uses the power of light-transport physics and a model trained on a small, initial seed dataset to reject approximately 99.6% of unwanted scenes. The pipeline is able to generalize to new scenes and degradations that can, in turn, be used to train existing models just like fully human-labeled data. Training on a dataset collected through this procedure leads to significant improvements on multiple existing weather removal methods on a carefully human-collected test set of real-world weather effects. The dataset and code can be found in the following website: http://visual.ee.ucla.edu/wstream.htm/.
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification. We characterize this dataset and how it compares to the mainstream datasets for evaluating how well ImageNet-trained classification models generalize. Finally, we analyze the performance of 100 ImageNet classification models on our new test dataset. Our fine-grained analysis demonstrates that these models struggle on images with quality issues. To enable future extensions to this work, we share our new dataset with evaluation server at: https://VizWiz.org/tasks-and-datasets/image-classification.
DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors

Neural Radiance Field (NeRF) has exhibited outstanding three-dimensional (3D) reconstruction quality via the novel view synthesis from multi-view images and paired calibrated camera parameters. However, previous NeRF-based systems have been demonstrated under strictly controlled settings, with little attention paid to less ideal scenarios, including with the presence of noise such as exposure, illumination changes, and blur. In particular, though blur frequently occurs in real situations, NeRF that can handle blurred images has received little attention. The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction. This leads to inconsistency and the degradation of the perceptual quality of the constructed scene. Hence, this paper proposes a DP-NeRF, a novel clean NeRF framework for blurred images, which is constrained with two physical priors. These priors are derived from the actual blurring process during image acquisition by the camera. DP-NeRF proposes rigid blurring kernel to impose 3D consistency utilizing the physical priors and adaptive weight proposal to refine the color composition error in consideration of the relationship between depth and blur. We present extensive experimental results for synthetic and real scenes with two types of blur: camera motion blur and defocus blur. The results demonstrate that DP-NeRF successfully improves the perceptual quality of the constructed NeRF ensuring 3D geometric and appearance consistency. We further demonstrate the effectiveness of our model with comprehensive ablation analysis. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/dogyoonlee/DP-NeRF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Project: https://dogyoonlee.github.io/dpNeRF/
Learning Geometric-Aware Properties in 2D Representation Using Lightweight CAD Models, or Zero Real 3D Pairs

Cross-modal training using 2D-3D paired datasets, such as those containing multi-view images and 3D scene scans, presents an effective way to enhance 2D scene understanding by introducing geometric and view-invariance priors into 2D features. However, the need for large-scale scene datasets can impede scalability and further improvements. This paper explores an alternative learning method by leveraging a lightweight and publicly available type of 3D data in the form of CAD models. We construct a 3D space with geometric-aware alignment where the similarity in this space reflects the geometric similarity of CAD models based on the Chamfer distance. The acquired geometric-aware properties are then induced into 2D features, which boost performance on downstream tasks more effectively than existing RGB-CAD approaches. Our technique is not limited to paired RGB-CAD datasets. By training exclu-sively on pseudo pairs generated from CAD-based reconstruction methods, we enhance the performance of SOTA 2D pretrained models that use ResNet-50 or ViT-B back-bones on various 2D understanding tasks. We also achieve comparable results to SOTA methods trained on scene scans on four tasks in NYUv2, SUNRGB-D, indoor ADE20k, and indoor/outdoor COCO, despite using lightweight CAD models or pseudo data. Please visit our page: https://GeoAware2dRepUsingCAD.github.io/