Reviewerly/exHarmony huggingface dataset:
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time consuming to annotate. Our method relies on the power of recent GANs to generate realistic images. We show how the GAN latent code can be decoded to produce a semantic segmentation of the image. Training the decoder only needs a few labeled examples to generalize to the rest of the latent space, resulting in an infinite annotated dataset generator! These generated datasets can then be used for training any computer vision architecture just as real datasets are. As only a few images need to be manually segmented, it becomes possible to annotate images in extreme detail and generate datasets with rich object and part segmentations. To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts. Our approach outperforms all semi-supervised baselines significantly and is on par with fully supervised methods, which in some cases require as much as 100x more annotated data as our method.
General Instance Distillation for Object Detection

In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge extracted from cumbersome teacher models. However, previous distillation methods of detection have weak generalization for different detection frameworks and rely heavily on ground truth (GT), ignoring the valuable relation information between instances. Thus, we propose a novel distillation method for detection tasks based on discriminative instances without considering the positive or negative distinguished by GT, which is called general instance distillation (GID). Our approach contains a general instance selection module (GISM) to make full use of feature-based, relation-based and response-based knowledge for distillation. Extensive results demonstrate that the student model achieves significant AP improvement and even outperforms the teacher in various detection frame-works. Specifically, RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based teacher model with 38.1% AP.
PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pre-trained features with simple anomaly detection and segmentation methods convincingly outperforms, much more complex, state-of-the-art methods.In order to obtain further performance gains in anomaly detection, we adapt pre-trained features to the target distribution. Although transfer learning methods are well established in multi-class classification problems, the one-class classification (OCC) setting is not as well explored. It turns out that naive adaptation methods, which typically work well in supervised learning, often result in catastrophic collapse (feature deterioration) and reduce performance in OCC settings. A popular OCC method, DeepSVDD, advocates using specialized architectures, but this limits the adaptation performance gain. We propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. Our method, PANDA, outperforms the state-of-the-art in the OCC, outlier exposure and anomaly segmentation settings by large margins <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles

In the Internet of Things enabled intelligent transportation systems, a huge amount of vehicle video data has been generated and real-time and accurate video analysis are very important and challenging work, especially in situations with complex street scenes. Therefore, we propose edge computing based video pre-processing to eliminate the redundant frames, so that we migrate the partial or all the video processing task to the edge, thereby diminishing the computing, storage and network bandwidth requirements of the cloud center, and enhancing the effectiveness of video analyzes. To eliminate the redundancy of the traffic video, the magnitude of motion detection based on spatio-temporal interest points (STIP) and the multi-modal linear features combination are presented which splits a video into super frame segments of interests. After that, we select the key frames from these interesting segments of the long videos with the design and detection of the prominent region. Finally, the extensive numerical experimental verification results show our methods are superior to the previous algorithms for different stages of the redundancy elimination, video segmentation, key frame selection and vehicle detection.
A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over- or under-confidence, i.e. are badly calibrated. To overcome this, many researchers have been working on understanding and quantifying uncertainty in a neural network’s prediction. As a result, different types and sources of uncertainty have been identified and various approaches to measure and quantify uncertainty in neural networks have been proposed. This work gives a comprehensive overview of uncertainty estimation in neural networks, reviews recent advances in the field, highlights current challenges, and identifies potential research opportunities. It is intended to give anyone interested in uncertainty estimation in neural networks a broad overview and introduction, without presupposing prior knowledge in this field. For that, a comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and irreducible data uncertainty is presented. The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks (BNNs), ensemble of neural networks, and test-time data augmentation approaches is introduced and different branches of these fields as well as the latest developments are discussed. For a practical application, we discuss different measures of uncertainty, approaches for calibrating neural networks, and give an overview of existing baselines and available implementations. Different examples from the wide spectrum of challenges in the fields of medical image analysis, robotics, and earth observation give an idea of the needs and challenges regarding uncertainties in the practical applications of neural networks. Additionally, the practical limitations of uncertainty quantification methods in neural networks for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods is given.
FedCPF: An Efficient-Communication Federated Learning Approach for Vehicular Edge Computing in 6G Communication Networks

The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Furthermore, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including “Customized”, “Partial”, and “Flexible”, known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times.
An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation

For human-robot collaboration (HRC), one of the most practical methods to ensure human safety with a vision-based system is establishing a minimum safe distance. This study proposes a novel integrated mixed reality (MR) system for safety-aware HRC using deep learning and digital twin generation. The proposed approach can accurately measure the minimum safe distance in real-time and provide MR-based task assistance to the human operator. The approach integrates MR with safety-related monitoring by tracking the shared workplace and providing user-centric visualization through smart MR glasses for safe and effective HRC. Two RGB-D sensors are used to reconstruct and track the working environment. One sensor scans one area of the physical environment through 3D point cloud data. The other also scans another area of the environment and tracks the user's 3D skeletal information. In addition, the two partially scanned environments are registered together by applying a fast global registration method to two sets of the 3D point cloud. Furthermore, deep learning-based instance segmentation is applied to the target object's 3D point cloud to increase the registration between the real robot and its virtual robot, the digital twin of the real robot. While only 3D point cloud data are widely used in previous studies, this study proposes a simple yet effective 3D offset-based safety distance calculation method based on the robot's digital twin and the human skeleton. The 3D offset-based method allows for real-time applicability without sacrificing the accuracy of safety distance calculation for HRI. In addition, two comparative evaluations were conducted to confirm the originality and advantage of the proposed MR-based HRC.
AutoFormer: Searching Transformers for Visual Recognition

Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/Cream.
YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection

To solve object detection issues in infrared images, such as a low recognition rate and a high false alarm rate caused by long distances, weak energy, and low resolution, we propose a region-free object detector named YOLO-FIR for infrared (IR) images with YOLOv5 core by compressing channels, optimizing parameters, etc. An improved infrared image object detection network, YOLO-FIRI, is further developed. Specifically, while designing the feature extraction network, the cross-stage-partial-connections (CSP) module in the shallow layer is expanded and iterated to maximize the use of shallow features. In addition, an improved attention module is introduced in residual blocks to focus on objects and suppress background. Moreover, multiscale detection is added to improve small object detection accuracy. Experimental results on the KAIST and FLIR datasets show that YOLO-FIRI demonstrates a qualitative improvement compared with the state-of-the-art detectors. Compared with YOLOv4, the mean average precision (mAP50) of YOLO-FIRI is increased by 21% on the KAIST dataset, the speed is reduced by 62%, the parameters are decreased by 89%, the weight size is reduced by more than 94%, and the computational costs are reduced by 84%. Compared with YOLO-FIR, YOLO-FIRI has an approximately 5% to 20% improvement in AP, AR (average recall), mAP50, F1, and mAP50:75. Furthermore, due to the shortcomings of high noise and weak features, image fusion can be applied to image preprocessing as a data enhancement method by fusing visible and infrared images based on a convolutional neural network.
A Subjective and Objective Study of Stalling Events in Mobile Streaming Videos

Over-the-top mobile adaptive video streaming is invariably influenced by volatile network conditions, which can cause playback interruptions (stalling or rebuffering events) and bitrate fluctuations, thereby impairing users' quality of experience (QoE). Video quality assessment models that can accurately predict users' QoE under such volatile network conditions are rapidly gaining attention, since these methods could enable more efficient design of quality control protocols for media-driven services such as YouTube, Amazon, Netflix, and many others. However, the development of improved QoE prediction models requires data sets of videos afflicted with diverse stalling events that have been labeled with ground-truth subjective opinion scores. Toward this end, we have created a new mobile video quality database that we call LIVE Mobile Stall Video Database-II. Our database contains a total of 174 videos afflicted with distortions caused by 26 different stalling patterns. We describe the way we simulated the diverse stalling events to create a corpus of distorted videos, and we detail the human study we conducted to obtain continuous-time subjective scores from 54 subjects. We also present the outcomes of our comprehensive analysis of the impact of several factors that influence subjective QoE, and report the performance of existing QoE-prediction models on our data set. We are making the database (videos, subjective data, and video metadata) publicly available in order to help the advance state-of-the-art research on user-centric mobile network planning and management. The database may be accessed at http://live.ece.utexas.edu/research/LIVEStallStudy/liveMobile.html.
Outsourcing Service Fair Payment Based on Blockchain and Its Applications in Cloud Computing

As a milestone in the development of outsourcing services, cloud computing enables an increasing number of individuals and enterprises to enjoy the most advanced services from outsourcing service providers. Because online payment and data security issues are involved in outsourcing services, the mutual distrust between users and service providers may severely impede the wide adoption of cloud computing. Nevertheless, most existing solutions only consider a specific type of services and rely on a trusted third-party to realize fair payment. In this paper, to realize secure and fair payment of outsourcing services in general without relying on any third-party, trusted or not, we introduce BPay, an outsourcing service fair payment framework based on blockchain in cloud computing. We first propose the system architecture, adversary model and design goals of BPay, then describe the design details. Our security and compatibility analysis indicates that BPay achieves soundness and robust fairness and it is compatible with the Bitcoin blockchain and the Ethereum blockchain. The key to the robust fairness and compatibility lies in an all-or-nothing checking-proof protocol and a top-down checking method. In addition, our experimental results show that BPay is computationally efficient. Finally, we present the applications of BPay in outsourcing services.
MDS Codes With Hulls of Arbitrary Dimensions and Their Quantum Error Correction

The hull of linear codes has promising utilization in coding theory and quantum coding theory. In this paper, we study the hull of generalized Reed-Solomon codes and extended generalized Reed-Solomon codes over finite fields with respect to the Euclidean inner product. Several infinite families of MDS codes with hulls of arbitrary dimensions are presented. As an application, using these MDS codes with hulls of arbitrary dimensions, we construct several new infinite families of entanglement-assisted quantum error-correcting codes with flexible parameters.
ATTS2S-VC: Sequence-to-sequence Voice Conversion with Attention and Context Preservation Mechanisms

This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving sequence modeling such as speech synthesis and recognition, machine translation, and image captioning. In contrast to current VC techniques, our method 1) stabilizes and accelerates the training procedure by considering guided attention and proposed context preservation losses, 2) allows not only spectral envelopes but also fundamental frequency contours and durations of speech to be converted, 3) requires no context information such as phoneme labels, and 4) requires no time-aligned source and target speech data in advance. In our experiment, the proposed VC framework can be trained in only one day, using only one GPU of an NVIDIA Tesla K80, while the quality of the synthesized speech is higher than that of speech converted by Gaussian mixture model-based VC and is comparable to that of speech generated by recurrent neural network-based text-to-speech synthesis, which can be regarded as an upper limit on VC performance.
A two-phase transfer learning model for cross-project defect prediction

Context: Previous studies have shown that a transfer learning model, TCA+ proposed by Nam et al., can significantly improve the performance of cross-project defect prediction (CPDP). TCA+ achieves the improvement by reducing data distribution difference between source (training data) and target (testing data) projects. However, TCA+ is unstable, i.e., its performance varies largely when using different source projects to build prediction models. In practice, it is hard to choose a suitable source project to build the prediction model. Objective: To address the limitation of TCA+, we propose a two-phase transfer learning model (TPTL) for CPDP. Method: In the first phase, we propose a source project estimator (SPE) to automatically choose two source projects with the highest distribution similarity to a target project from candidates. Next, two source projects that are estimated to achieve the highest values of F1-score and cost-effectiveness are selected. In the second phase, we leverage TCA+ to build two prediction models based on the two selected projects and combine their prediction results to further improve the prediction performance. Results: We evaluate TPTL on 42 defect datasets from PROMISE repository, and compare it with two versions of TCA+ (TCA+_Rnd, randomly selecting one source project; TCA+_All, using all alternative source projects), a related source project selection model TDS proposed by Herbold, a state-of-the-art CPDP model leveraging a log transformation (LT) method, and a transfer learning model Dycom with better form of TCA. Experiment results show that, on average across 42 datasets, TPTL respectively improves these baseline models by 19%, 5%, 36%, 27%, and 11% in terms of F1-score; by 64%, 92%, 71%, 11%, and 66% in terms of cost-effectiveness. Conclusion: The proposed TPTL model can solve the instability problem of TCA+, showing substantial improvements over the state-of-the-art and related CPDP models.
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists' workload and simultaneously reduce misdiagnosis rate. However, the automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method is corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumor localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.