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Real-time and Robust System for Counting Movement-Specific Vehicle at Crowded Intersections

Real-time and Robust System for Counting Movement-Specific V...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vu-Hoang Tran Le-Hoai-Hieu Dang Chinh-Nghiep Nguyen Ngoc-Hoang-Lam Le Khanh-Phong Bui Lam-Truong Dam Quang-Thang Le Dinh-Hiep Huynh Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

In order to reduce traffic congestion and improve the efficiency of traffic light signals, intelligent traffic systems are being developed by researchers, and vehicle counting is one of the key techniques in the system. The traditional methods mostly focus on increasing the vehicle counting effectiveness without regard to the program execution efficiency. The practical value of these systems will be reduced if they cannot be operated in real-time on compact IoT device. Therefore, in this paper, we mainly focus on designing a real-time and robust system for the problem of counting specific-movement vehicles. The system is able to detect and track objects in the area of interest, then count those tracked trajectories using the movements. To improve performance of tracking multiple objects, a high recall detection method and an efficient feature matching strategy were proposed. Moreover, to minimize the wrong direction of movement prediction and improve the results of vehicle counting, a cosine similarity-based vehicle counting scheme is applied. Experiments are conducted on AI City 2021 Track-1 dataset. Our method is evaluated on both sides of efficiency and effectiveness.

关键词： Performance evaluation computer vision Tracking conferences Urban areas Feature extraction Real-time systems

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Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies

Do Deepfakes Feel Emotions? A Semantic Approach to Detecting...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hosler, Brian Salvi, Davide Murray, Anthony Antonacci, Fabio Bestagini, Paolo Tubaro, Stefano Stamm, Matthew C. Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781665448994

Recent advances in deep learning and computer vision have spawned a new class of media forgeries known as deepfakes, which typically consist of artificially generated human faces or voices. The creation and distribution of deepfakes raise many legal and ethical concerns. As a result, the ability to distinguish between deepfakes and authentic media is vital. While deepfakes can create plausible video and audio, it may be challenging for them to to generate content that is consistent in terms of high-level semantic features, such as emotions. Unnatural displays of emotion, measured by features such as valence and arousal, can provide significant evidence that a video has been synthesized. In this paper, we propose a novel method for detecting deepfakes of a human speaker using the emotion predicted from the speaker's face and voice. The proposed technique leverages Long Short-Term Memory (LSTM) networks that predict emotion from audio and video Low-Level Descriptors (LLDs). Predicted emotion in time is used to classify videos as authentic or deepfakes through an additional supervised classifier.

关键词： computer vision Emotion recognition Law Semantics Speech recognition Media Forgery

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NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bhat, Goutam Danelljan, Martin Timofte, Radu Akita, Kazutoshi Cho, Wooyeong Fan, Haoqiang Jia, Lanpeng Kim, Daeshik Lecouat, Bruno Li, Youwei Liu, Shuaicheng Liu, Ziluan Luo, Ziwei Maeda, Takahiro Mairal, Julien Micheloni, Christian Mo, Xuan Oba, Takeru Ostyakov, Pavel Ponce, Jean Son, Sanghyeok Sun, Jian Ukita, Norimichi Muhammad, Rao Yan, Umer Youliang Yu, Lei Zhussip, Magauiya Zou, Xueyi Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Huawei Noahs Ark Lab Hong Kong Peoples R China Huawei CBG AITA Hong Kong Peoples R China Megvii Technol Beijing Peoples R China INRIA Rocquencourt France Toyota Technol Inst Toyota Japan Univ Udine Udine Italy KAIST Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks;Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task.

关键词： computer vision conferences Superresolution Merging Cameras pattern recognition Noise measurement

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty conferences Estimation Probability distribution pattern recognition

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Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Evaluating Deep Neural Networks Trained on Clinical Images i...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Groh, Matthew Harris, Caleb Soenksen, Luis Lau, Felix Han, Rachel Kim, Aerin Koochek, Arash Badri, Omar MIT Media Lab Cambridge MA 02139 USA Harvard Univ MIT Cambridge MA 02139 USA Scale San Francisco CA USA Banner Hlth Phoenix AZ USA Northeast Dermatol Associates Beverly MA USA

ISBN: (纸本)9781665448994

How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.

关键词： Deep learning Training computer vision Image segmentation Dermatology Neural networks Training data

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A Spacecraft Dataset for Detection, Segmentation and Parts recognition

A Spacecraft Dataset for Detection, Segmentation and Parts R...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hoang Anh Dung Chen, Bo Chin, Tat-Jun Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665448994

Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, over the decades, the world witnessed the growing use of deep learning in solving problems for space applications, such as self-driving robot, tracers, insect-like robot on cosmos and health monitoring of spacecraft. These are just some prominent examples that has advanced space industry with the help of deep learning. However, the success of deep learning models requires a lot of training data in order to have decent performance, while on the other hand, there are very limited amount of publicly available space datasets for the training of deep learning models. Currently, there is no public datasets for space-based object detection or instance segmentation, partly because manually annotating object segmentation masks is very time consuming as they require pixel-level labelling, not to mention the challenge of obtaining images from space. In this paper, we aim to fill this gap by releasing a dataset for spacecraft detection, instance segmentation and part recognition. The main contribution of this work is the development of the dataset using images of space stations and satellites, with rich annotations including bounding boxes of spacecrafts and masks to the level of object parts, which are obtained with a mixture of automatic processes and manual efforts. We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset. The link for downloading the proposed dataset can be found on https://***/Yurushia1998/SatelliteDataset.

关键词： Space vehicles Deep learning Image segmentation computer vision Satellites Service robots Object detection

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EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

EfficientViT-SAM: Accelerated Segment Anything Model Without...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhuoyang Zhang Han Cai Song Han Tsinghua University NVIDIA MIT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM’s lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT’s efficiency and capacity, EfficientViT-SAM delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://***/mit-han-lab/efficientvit.

关键词： Training Image segmentation computer vision Codes conferences Computational modeling Graphics processing units

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Selective Replay Enhances Learning in Online Continual Analogical Reasoning

Selective Replay Enhances Learning in Online Continual Analo...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hayes, Tyler L. Kanan, Christopher Rochester Inst Technol Rochester NY 14623 USA Paige Rochester NY USA Cornell Tech New York NY USA

ISBN: (纸本)9781665448994

In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task(1).

关键词： Measurement Protocols Neural networks Reinforcement learning Streaming media Cognition pattern recognition

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A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks

A Bop and Beyond: A Second Order Optimizer for Binarized Neu...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Daniel Suarez-Ramirez, Cuauhtemoc Gonzalez-Mendoza, Miguel Chang, Leonardo Ochoa-Ruiz, Gilberto Alberto Duran-Vega, Mario Tecnol Monterrey Sch Engn & Sci Dept Comp Sci Monterrey NL Mexico

ISBN: (纸本)9781665448994

The optimization of Binary Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation;thus, efforts are focused adapting full-precision techniques to work on BNNs. In the literature, only one previous effort has tackled the problem of directly training the BNNs with bit-flips by using the first raw moment estimate of the gradients and comparing it against a threshold for deciding when to flip a weight (Bop). In this paper, we take an approach parallel to Adam which also uses the second raw moment estimate to normalize the first raw moment before doing the comparison with the threshold, we call this method Bop2ndOrder. We present two versions of the proposed optimizer: a biased one and a bias-corrected one, each with its own applications. Also, we present a complete ablation study of the hyperparameters space, as well as the effect of using schedulers on each of them. For these studies, we tested the optimizer in CIFAR10 using the BinaryNet architecture. Also, we tested it in ImageNet 2012 with the XnorNet and BiRealNet architectures for accuracy. In both datasets our approach proved to converge faster, was robust to changes of the hyperparameters, and achieved better accuracy values.

关键词： Training computer vision conferences computer architecture Artificial neural networks pattern recognition Optimization

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On the Robustness and Generalizability of Face Synthesis Detection Methods

On the Robustness and Generalizability of Face Synthesis Det...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sabel, Johan Johansson, Fredrik Swedish Def Res Agcy FOI Stockholm Sweden

ISBN: (纸本)9781665448994

In recent years, significant progress has been made within human face synthesis. It is now possible, and easy for anyone, to generate credible high-resolution images of non-existing people. This calls for effective detection methods. In this paper, three state-of-the-art deep learning-based methods are evaluated with respect to their robustness and generalizability, which are two factors that must be taken into consideration for methods intended to be deployed in the wild. The robustness experiments show that it is possible to achieve near-perfect performance when discriminating between real and synthetic facial images that have been post-processed heavily with various perturbation techniques;especially when similar perturbations are incorporated during training of the detection models. The generalization experiments show that already trained detection models can achieve high performance on images from sources not known during training, provided that the models are fine-tuned on such images. One model achieved an average accuracy of 96.8% after being fine-tuned on 3 training images from each unknown source considered (one real and one synthetic source). However, additional images were required when fine-tuning using a different approach aimed at preventing catastrophic forgetting. Furthermore, in general, no method generalized well without fine-tuning. Hence, the limited generalization capability remains a shortcoming that must be overcome before the detection methods can be utilized in the wild.

关键词： Training Learning systems computer vision Perturbation methods Face recognition conferences Robustness

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