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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1081-1090 订阅

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SOFEA: A Non-iterative and Robust Optical Flow Estimation Algorithm for Dynamic vision Sensors

SOFEA: A Non-iterative and Robust Optical Flow Estimation Al...

引用

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

作者： Low, Weng Fei Gao, Zhi Xiang, Cheng Ramesh, Bharath Natl Univ Singapore 1 Inst Hlth Singapore Singapore

ISBN: (纸本)9781728193601

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively compute the continuous-time flow information of events produced from bio-inspired cameras such as the dynamic vision sensor (DVS). The output of a DVS is a stream of asynchronous spikes ("events"), transmitted at very minimal latency (110 mu s), caused by local brightness changes. Due to this unconventional output, a continuous representation of events over time is invaluable to most applications using the DVS. To this end, SOFEA consolidates the spatio-temporal information on the surface of active events for flow estimation in a single-shot manner, as opposed to iterative methods in the literature. In contrast to previous works, this is also the first principled method towards finding locally optimal set of neighboring events for plane fitting using an adaptation of Prim's algorithm. Consequently, SOFEA produces flow estimates that are more accurate across a wide variety of scenes compared to state-of-the-art methods. A direct application of such flow estimation is rendering sharp event images using the set of active events at a given time, which is further demonstrated and compared to existing works (source code will be made available at our homepage after the review process).

关键词： Iterative methods

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Moire pattern Removal via Attentive Fractal Network

Moire Pattern Removal via Attentive Fractal Network

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

作者： Xu, Dejia Chu, Yihao Sun, Qingyan Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (纸本)9781728193601

Moire patterns are commonly seen artifacts when taking photos of screens and other objects with high-frequency textures. It's challenging to remove the moire patterns considering its complex color and shape. In this work, we propose an Attentive Fractal Network to effectively solve this problem. First, we construct each Attentive Fractal Block with progressive feature fusion and channel-wise attention guidance. The network is then fractally stacked with the block on each of its levels. Second, to further boost the performance, we adopt a two-stage augmented refinement strategy. With these designs, our method wins the burst demoireing track and achieves second place in single image demoireing and single image deblurring tracks in NTIRE20 Challenges. Extensive experiments demonstrate the superiority of our method for moire pattern removal compared to existing state-of-the-art methods, and prove the effectiveness of its each component. We will publicly release our code and trained weights on https://***/ir1d/AFN.

关键词： Textures

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Defending Black Box Facial recognition Classifiers Against Adversarial Attacks

Defending Black Box Facial Recognition Classifiers Against A...

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

作者： Theagarajan, Rajkumar Bhanu, Bir Univ Calif Riverside Ctr Res Intelligent Syst Riverside CA 92521 USA

ISBN: (纸本)9781728193601

Defending adversarial attacks is a critical step towards reliable deployment of deep learning empowered solutions for biometrics verification. Current approaches for defending Black box models use the classification accuracy of the Black box as a performance metric for validating their defense. However, classification accuracy by itself is not a reliable metric to determine if the resulting image is "adversarial-free". This is a serious problem for online biometrics verification applications where the ground-truth of the incoming image is not known and hence we cannot compute the accuracy of the classifier or know if the image is "adversarial-free" or not. This paper proposes a novel framework for defending Black box systems from adversarial attacks using an ensemble of iterative adversarial image purifiers whose performance is continuously validated in a loop using Bayesian uncertainties. The proposed approach is (i) model agnostic, (ii) can convert single step black box defenses into an iterative defense and (iii) has the ability to reject adversarial examples. This paper uses facial recognition as a test case for validating the defense and experimental results on the MS-Celeb dataset show that the proposed approach can consistently detect adversarial examples and purify/reject them against a variety of adversarial attacks with different ranges of perturbations.

关键词： Deep learning

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Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing

Understanding Knowledge Gaps in Visual Question Answering: I...

引用

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

作者： Bajaj, Goonmeet Bandyopadhyay, Bortik Schmidt, Daniel Maneriker, Pranav Myers, Christopher Parthasarathy, Srinivasan Ohio State Univ OSU Columbus OH 43210 USA Wright State Univ Dayton OH 45435 USA Air Force Res Lab AFRL Wright Patterson AFB OH USA

ISBN: (纸本)9781728193601

Traditional Visual Question Answering (VQA) datasets typically contain questions related to the spatial information of objects, object attributes, or general scene questions. Recently, researchers have recognized the need to improve the balance of such datasets to reduce the system's dependency on memorized linguistic features and statistical biases, while aiming for enhanced visual understanding. However, it is unclear whether any latent patterns exist to quantify and explain these failures. As an initial step towards better quantifying our understanding of the performance of VQA models, we use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs. Each KG describes the reasoning abilities needed to arrive at a resolution, and failure to resolve gaps indicates an absence of the required reasoning ability. After identifying KGs for each question, we examine the skew in the distribution of questions for each KG. We then introduce a targeted question generation model to reduce this skew, which allows us to generate new types of questions for an image.

关键词： computer vision

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Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis

Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint...

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

作者： Von Braun, Marie-Sophie Frenzel, Patrick Kaeding, Christian Fuchs, Mirco Leipzig Univ Appl Sci Lab Biosignal Proc Leipzig Germany Inst Appl Training Sci Leipzig Leipzig Germany

ISBN: (纸本)9781728193601

Determining a waterline in images recorded in canoe sprint training is an important component for the kinematic parameter analysis to assess an athlete's performance. Here, we propose an approach for the automated waterline detection. First, we utilized a pre-trained Mask R-CNN by means of transfer learning for canoe segmentation. Second, we developed a multi-stage approach to estimate a waterline from the outline of the segments. It consists of two linear regression stages and the systematic selection of canoe parts. We then introduced a parameterization of the waterline as a basis for further evaluations. Next, we conducted a study among several experts to estimate the ground truth waterlines. This not only included an average waterline drawn from the individual experts annotations but, more importantly, a measure for the uncertainty between individual results. Finally, we assessed our method with respect to the question whether the predicted waterlines are in accordance with the experts annotations. Our method demonstrated a high performance and provides opportunities for new applications in the field of automated video analysis in canoe sprint.

关键词： computer vision

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Adversarial Fooling Beyond "Flipping the Label"

Adversarial Fooling Beyond "Flipping the Label"

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

作者： Mopuri, Konda Reddy Shaj, Vaisakh Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Benguluru India

ISBN: (纸本)9781728193601

Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, they are quite vulnerable to adversarial attacks. These attacks are potentially dangerous in real-life deployments. Though there have been many adversarial attacks proposed in recent years, there is no proper way of quantifying the effectiveness of these attacks. As of today, mere fooling rate is used for measuring the susceptibility of the models, or the effectiveness of adversarial attacks. Fooling rate just considers label flipping and does not consider the cost of such flipping, for instance, in some deployments, flipping between two species of dogs may not be as severe as confusing a dog category with that of a vehicle. Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well. In this work we first bring out the drawbacks of the existing evaluation and propose novel metrics to capture various aspects of the fooling. Further, for the first time, we present a comprehensive analysis of several important adversarial attacks over a set of distinct CNN architectures. We believe that the presented analysis brings valuable insights about the current adversarial attacks and the CNN models.

关键词： computer vision

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Multi-scale Grouped Dense Network for VVC Intra Coding

Multi-scale Grouped Dense Network for VVC Intra Coding

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

作者： Li, Xin Sun, Simeng Zhang, Zhizheng Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams "IMC" and "haha" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.

关键词： Image coding conferences Generative adversarial networks Video coding Transform coding computer vision pattern recognition

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Dynamic Attention-based Visual Odometry

Dynamic Attention-based Visual Odometry

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

作者： Kuo, Xin-Yu Liu, Chien Lin, Kai-Chen Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781728193601

This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the relative importance of different semantic regions in input frames for pose estimation. In order to examine the proposed DAVO, we perform a number of experiments on the KITTI Visual Odometry and SLAM benchmark suite to quantitatively and qualitatively inspect the impacts of the dynamically adjusted weights on the accuracy of the evaluated trajectories. Moreover, we design a set of ablation analyses to justify each of our design choices, and validate the effectiveness as well as the advantages of DAVO. Our experiments on the KITTI dataset shows that the proposed DAVO framework does provide satisfactory performance in ego-motion estimation, and is able deliver competitive performance when compared to the contemporary VO methods.

关键词： computer vision

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A Non-invasive vision Based Approach to Velocity Measurement of Skeleton Training

A Non-invasive Vision Based Approach to Velocity Measurement...

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

作者： Evans, Murray Needham, Laurie Colyer, Steffi L. Cosker, Darren P. Univ Bath Dept Comp Sci Ctr Anal Mot Entertainment Res & Applicat CAMERA Bath Avon England Univ Bath Dept Hlth CAMERA Bath Avon England Univ Bath Dept Comp Sci CAMERA Bath Avon England

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Skeleton is a winter sport where performance is greatly affected by the velocity an athlete can achieve during their start up to the point where they load themselves onto their sled. As such, it is of interest to athletes and coaching staff to be able to monitor the performance of their athletes and how they respond to different training schedules and techniques. This paper proposes a non-invasive vision based method for measuring the velocity of a skeleton athlete and their sled during the push start. Mean differences in estimated velocity between ground truth data and our proposed system were -0.005 (+/- 0.186) m.s(-1) for the athlete mass centre and -0.017 (+/- 0.133) m.s(-1) for the sled. The results compare favourably to techniques previously presented in the biomechanics and sport science literature.

关键词： Cameras Skeleton Image segmentation Head Three-dimensional displays Torso Tracking

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Classification-aware Semi-supervised Domain Adaptation

Classification-aware Semi-supervised Domain Adaptation

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

作者： He, Gewen Liu, Xiaofeng Fan, Fangfang You, Jane Florida State Univ Tallahassee FL 32306 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA HK PolyU Hong Kong Peoples R China

ISBN: (纸本)9781728193601

Deep neural networks are usually data-starved, but manually annotation can be costly in many specific tasks. For instance, the emotion recognition from the audio. However, there is a large amount of public available labeled image-based facial expression recognition datasets. How could these images help for the audio emotion recognition with limited labeled data according to their inherent correlations can be a meaningful and challenging task. In this paper, we propose a semi-supervised adversarial network that allows the knowledge transfer from the labeled videos to the heterogeneous labeled audio domain hence enhancing the audio emotion recognition performance. Specifically, face image samples are translated to the spectrograms class-wisely. To harness the translated samples in a sparsely distributed area and construct a tighter decision boundary, we propose to precisely estimate the density on feature space and incorporate the reliable low-density sample with an annealing scheme. Moreover, the unlabeled audios are collected with the high-density path in a graph representation. As a possible "recognition via generation" framework, we empirically demonstrated its effectiveness on several audio emotional recognition benchmarks. We also demonstrated its generality on recent large-scaled semi-supervised domain adaptation tasks.

关键词： Emotion recognition

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