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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521-530 订阅

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Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix

Strengthening the Transferability of Adversarial Examples Us...

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

作者： Jang, Donggon Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding malicious noise imperceptible to a human. The adversarial examples successfully fool the models under the white-box setting, but the performance of attacks under the black-box setting degrades significantly, which is known as the low transferability problem. Various methods have been proposed to improve transferability, yet they are not effective against adversarial training and defense models. In this paper, we introduce two new methods termed Lookahead Iterative Fast Gradient Sign Method (LI-FGSM) and Self-CutMix (SCM) to address the above issues. LI-FGSM updates adversarial perturbations with the accumulated gradient obtained by looking ahead. A previous gradient-based attack is used for looking ahead during N steps to explore the optimal direction at each iteration. It allows the optimization process to escape the sub-optimal region and stabilize the update directions. SCM leverages the modified CutMix, which copies a patch from the original image and pastes it back at random positions of the same image, to preserve the internal information. SCM makes it possible to generate more transferable adversarial examples while alleviating the overfitting to the surrogate model employed. Our two methods are easily incorporated with the previous iterative gradient-based attacks. Extensive experiments on ImageNet show that our approach acquires state-of-the-art attack success rates not only against normally trained models but also against adversarial training and defense models.

关键词： Training Deep learning computer vision Computational modeling Perturbation methods conferences Neural networks

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Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

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2023 ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@cvpr 2023. © 2023 ieee.

关键词： Iterative methods

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SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos

SoccerTrack: A Dataset and Tracking Algorithm for Soccer wit...

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

作者： Scott, Atom Uchida, Ikuma Onishi, Masaki Kameda, Yoshinari Fukui, Kazuhiro Fujii, Keisuke Univ Tsukuba Tsukuba Ibaraki Japan Natl Inst Adv Ind Sci & Technol Tsukuba Japan Nagoya Univ Nagoya Aichi Japan RIKEN JST PRESTO Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking devices that can track both players and balls are critical to the performance of sports teams. Recently, significant effort has been focused on building larger broadcast sports video datasets. However, broadcast videos do not show the entire pitch and only provides partial information about the game. On the other hand, other camera perspectives can capture the whole field in a single frame, such as fish-eye and bird-eye view (drone) cameras. Unfortunately, there has not been a dataset where such data has been publicly shared until now. This paper proposes SoccerTrack, a dataset set consisting of GNSS and bounding box tracking data annotated on video captured with a 8K-resolution fish-eye camera and a 4K-resolution drone camera. In addition to a benchmark tracking algorithm, we include code for camera calibration and other preprocessing. Finally, we evaluate the tracking accuracy among a GNSS, fish-eye camera and drone camera data. SoccerTrack is expected to provide a more robust foundation for designing MOT algorithms that are less reliant on visual cues and more reliant on motion analysis.

关键词： Performance evaluation Global navigation satellite system Visualization Codes Tracking Cameras pattern recognition

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Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation

Auxiliary Learning for Self-Supervised Video Representation ...

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

作者： Dadashzadeh, Amirhossein Whone, Alan Mirmehdi, Majid Univ Bristol Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distils its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51.

关键词： Representation learning Knowledge engineering Training Predictive models Data models pattern recognition Reliability

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TikTok for good: Creating a diverse emotion expression database

TikTok for good: Creating a diverse emotion expression datab...

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

作者： Surabhi, Saimourya Shah, Bhavik Washington, Peter Mutlu, Onur Cezmi Leblanc, Emilie Mohite, Prathamesh Husic, Arman Kline, Aaron Dunlap, Kaitlyn McNealis, Maya Liu, Bennett Deveaux, Nick Sleiman, Essam Wall, Dennis P. Stanford Univ Dept Biomed Data Sci Dept Pediat Syst Med Stanford CA 94305 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition (FER) is a critical computer vision task for a variety of applications. Despite the widespread use of FER, there is a dearth of racially diverse facial emotion datasets which are enriched for children, teens, and adults. To bridge this gap, we have built a diverse expression recognition database using publicly available videos from TikTok, a video-focused social networking service. We describe the construction of the TikTok Facial expression recognition (FER) database. The dataset is extracted from 6428 videos scraped from TikTok. The videos consist of 9392 distinct individuals and labels for 15 emotion-related prompts. We were able to achieve a F1 score 0.78 for Ekman emotions on expression classification using transfer learning. We hope that the scale and diversity of the TikTokFER dataset will be of use to affective computing practitioners.

关键词： Bridges computer vision Emotion recognition Databases Social networking (online) Face recognition conferences

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Multi-level Domain Adaptation for Lane Detection

Multi-level Domain Adaptation for Lane Detection

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

作者： Li, Chenguang Zhang, Boheng Shi, Jia Cheng, Guangliang SenseTime Res Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, i.e. TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.

关键词： computer vision Costs Lane detection Shape Image edge detection conferences Semantics

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3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks

3DRRDB: Super Resolution of Multiple Remote Sensing Images u...

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

作者： Ramzy Ibrahim, Mohamed Benavente, Robert Lumbreras, Felipe Ponsa, Daniel Arab Acad Sci & Technol Comp Engn Dept Alexandria Egypt Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain Comp Vis Ctr Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.

关键词： Training Solid modeling Three-dimensional displays PSNR Convolution Superresolution pattern recognition

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Where did I leave my keys? - Episodic-Memory-Based Question Answering on Egocentric Videos

Where did I leave my keys? - Episodic-Memory-Based Question ...

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

作者： Baermann, Leonard Waibel, Alex Karlsruhe Inst Technol Interact Syst Lab Karlsruhe Germany

ISBN: (纸本)9781665487399

Humans have a remarkable ability to organize, compress and retrieve episodic memories throughout their daily life. Current AI systems, however, lack comparable capabilities as they are mostly constrained to an analysis with access to the raw input sequence, assuming an unlimited amount of data storage which is not feasible in realistic deployment scenarios. For instance, existing Video Question Answering (VideoQA) models typically reason over the video while already being aware of the question, thus requiring to store the complete video in case the question is not known in advance. yIn this paper, we address this challenge with three main contributions: First, we propose the Episodic Memory Question Answering (EMQA) task as a specialization of VideoQA. Specifically, EMQA models are constrained to keep only a constant-sized representation of the video input, thus automatically limiting the computation requirements at query time. Second, we introduce a new egocentric VideoQA dataset called QAEGO4D, far larger than existing egocentric VideoQA datasets and featuring video length unprecedented in VideoQA datasets in general. Third, we present extensive experiments on the new dataset, comparing various baseline models in both the VideoQA and the EMQA setting. To facilitate future research on egocentric VideoQA as well as episodic memory representation and retrieval, we publish our code and dataset.

关键词： computer vision Limiting Codes Computational modeling conferences Memory management Question answering (information retrieval)

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Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators

Real-time Hyper-Dimensional Reconfiguration at the Edge usin...

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

作者： Kandaswamy, Indhumathi Farkya, Saurabh Daniels, Zachary van der Wal, Gooitzen Raghavan, Aswin Zhang, Yuzheng Hu, Jun Lomnitz, Michael Isnardi, Michael Zhang, David Piacentino, Michael SRI Int Ctr Vis Technol Princeton NJ 08540 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point Multiply-ACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators. We describe the algorithm, trained quantized model generation, and simulated performance of a feature extractor free of multiply-accumulates feeding a hyperdimensional logic-based classifier. Then we show how performance increases with the number of hyperdimensions. We describe the realized low-SWaP FPGA hardware and embedded software system compared to traditional DNNs and detail the implemented hardware accelerators. We discuss the measured system latency and power, noise robustness due to use of learnable quantization and HD computing, actual versus simulated system performance for a video activity classification task and demonstration of reconfiguration on this same dataset. We show that reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation (gradient-free), using few-shot learning of new classes at the edge. Initial work performed used LRCN DNN and is currently extended to use Two-stream DNN with improved performance.

关键词： Quantization (signal) Power measurement System performance Software algorithms Feature extraction Real-time systems pattern recognition

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Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning

Hybrid Consistency Training with Prototype Adaptation for Fe...

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

作者： Ye, Meng Lin, Xiao Burachas, Giedrius Divakaran, Ajay Yao, Yi SRI Int 333 Ravenswood Ave Menlo Pk CA 94025 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, representation learning, etc. However, FSL remains challenging due to the following longstanding difficulties. 1) The seen and unseen classes are disjoint, resulting in a distribution shift between training and testing. 2) During testing, labeled data of previously unseen classes is sparse, making it difficult to reliably extrapolate from labeled support examples to unlabeled query examples. To tackle the first challenge, we introduce Hybrid Consistency Training to jointly leverage two types of consistency: 1) interpolation consistency, which interpolates hidden features to imposes linear behavior locally, and 2) data augmentation consistency, which learns robust embeddings against sample variations. As for the second challenge, we use unlabeled examples to iteratively normalize features and adapt prototypes, as opposed to commonly used one-time update, for more reliable prototype-based transductive inference. We show that our method generates a 2% to 5% improvement over the state-of-the-art methods with similar backbones on five FSL datasets and, more notably, a 7% to 8% improvement for more challenging cross-domain FSL.

关键词： Training Representation learning Measurement Interpolation Prototypes Inference algorithms pattern recognition

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