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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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On the Exploitation of Deepfake Model recognition

On the Exploitation of Deepfake Model Recognition

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Luca Guarnera Oliver Giudice Matthias Nieß ner Sebastiano Battiato Department of Mathematics and Computer Science University of Catania Italy IT Dept. Banca d&#x2019 Italia Applied Research Team Rome Italy Technical University of Munich Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Despite recent advances in Generative Adversarial Networks (GANs), with special focus to the Deepfake phenomenon there is no a clear understanding neither in terms of explainability nor of recognition of the involved models. In particular, the recognition of a specific GAN model that generated the deepfake image compared to many other possible models created by the same generative architecture (e.g. StyleGAN) is a task not yet completely addressed in the state-of-the-art. In this work, a robust processing pipeline to evaluate the possibility to point-out analytic fingerprints for Deepfake model recognition is presented. After exploiting the latent space of 50 slightly different models through an in-depth analysis on the generated images, a proper encoder was trained to discriminate among these models obtaining a classification accuracy of over 96%. Once demonstrated the possibility to discriminate extremely similar images, a dedicated metric exploiting the insights discovered in the latent space was introduced. By achieving a final accuracy of more than 94% for the Model recognition task on images generated by models not employed in the training phase, this study takes an important step in countering the Deepfake phenomenon introducing a sort of signature in some sense similar to those employed in the multimedia forensics field (e.g. for camera source identification task, image ballistics task, etc).

关键词： Training Measurement Deepfakes Analytical models Image recognition Forensics Pipelines

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Complete and temporally consistent video outpainting

Complete and temporally consistent video outpainting

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Loï c Dehan Wiebe Van Ranst Patrick Vandewalle Toon Goedemé EAVISE-PSI-ESAT KU Leuven Sint-Katelijne-Waver Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

We describe a novel method for video outpainting. The goal of outpainting is to fill in missing regions at the edges of video frames. Our focus lies on converting portrait (9:16) to landscape (16:9) video. In contrast, most video completion research is focused on inpainting: filling a masked section within the frame based on the remaining, known *** proposed method consists of three main aspects: (1) We form a background estimation using video object segmentation and video inpainting methods, (2) we use optical flow to form temporal consistency, and (3) we propose image shifting to improve individual frame completions. Our method is able to successfully broaden the aspect ratio of a video. On most videos, we achieve realistic results. Only on videos with complex camera motion and foreground objects leaving the frame, the quality is *** contrast to other state-of-the-art methods, our method is able to reconstruct the full frame, including unseen image parts. Moreover, it is temporally consistent. We evaluate our method on the DAVIS and YouTube-VOS datasets. The code is publicly available 1 .

关键词： Measurement computer vision Image edge detection conferences Estimation Object segmentation Filling

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HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

HS-Pose: Hybrid Scope Feature Extraction for Category-level ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Linfang Zheng Chen Wang Yinghan Sun Esha Dasgupta Hua Chen Aleš Leonardis Wei Zhang Hyung Jin Chang Department of Mechanical and Energy Engineering Southern University of Science and Technology School of Computer Science University of Birmingham Department of Computer Science The University of Hong Kong Peng Cheng Laboratory Shenzhen China

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5°2cm metric and 10.3% on IoU 75 . Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5°2cm, 6.9% on IoU 75 ) on REAL275 dataset and runs in real-time (50 FPS) 1 1 Codeisavailable: https://***/Lynne-Zheng-Linfang/HS-Pose.

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Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus

Semi-Supervised Stereo-Based 3D Object Detection via Cross-V...

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conference on computer vision and pattern recognition (CVPR)

作者： Wenhao Wu Hau-San Wong Si Wu Department of Computer Science City University of Hong Kong School of Computer Science and Engineering South China University of Technology

Stereo-based 3D object detection, which aims at detecting 3D objects with stereo cameras, shows great potential in low-cost deployment compared to LiDAR-based methods and excellent performance compared to monocular-based algorithms. However, the impressive performance of stereo-based 3D object detection is at the huge cost of high-quality manual annotations, which are hardly attainable for any given scene. Semi-supervised learning, in which limited annotated data and numerous unannotated data are required to achieve a satisfactory model, is a promising method to address the problem of data deficiency. In this work, we propose to achieve semi-supervised learning for stereo-based 3D object detection through pseudo annotation generation from a temporal-aggregated teacher model, which temporally accumulates knowledge from a student model. To facilitate a more stable and accurate depth estimation, we introduce Temporal-Aggregation-Guided (TAG) disparity consistency, a cross-view disparity consistency constraint between the teacher model and the student model for robust and improved depth estimation. To mitigate noise in pseudo annotation generation, we propose a cross-view agreement strategy, in which pseudo annotations should attain high degree of agreements between 3D and 2D views, as well as between binocular views. We perform extensive experiments on the KITTI 3D dataset to demonstrate our proposed method's capability in leveraging a huge amount of unannotated stereo images to attain significantly improved detection results.

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcus Valtonen Ö rnhag Patrik Persson Må rten Wadenbä ck Kalle Å strö m Anders Heyden Centre for Mathematical Sciences Lund University Department of Electrical Engineering Link&#x00F6 ping University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated *** proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods. Code available at: https://***/marcusvaltonen/DronePoseLib. 1

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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Slimmable Video Codec

Slimmable Video Codec

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhaocheng Liu Luis Herranz Fei Yang Saiping Zhang Shuai Wan Marta Mrak Marc Gó rriz Blanch School of Electronics and Information Northwestern Polytechnical University Xi&#x2019 an China Computer Vision Center Universitat Autonoma de Barcelona Barcelona Spain State Key Laboratory of Integrated Services Networks Xidian University Xi&#x2019 The Lighthouse White City Place 201 Wood Lane BBC Research &#x0026 Development London UK

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural video compression has emerged as a novel paradigm combining trainable multilayer neural net-works and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression.

关键词： Computational modeling Memory management Memory architecture Rate-distortion Machine learning Video compression Nonhomogeneous media

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DropKey for vision Transformer

DropKey for Vision Transformer

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conference on computer vision and pattern recognition (CVPR)

作者： Bonan Li Yinhan Hu Xuecheng Nie Congying Han Xiangjian Jiang Tiande Guo Luoqi Liu University of Chinese Academy of Sciences MT Lab Meitu Inc. University of Cambridge

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel Drop-Key method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

MIANet: Aggregating Unbiased Instance and General Informatio...

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conference on computer vision and pattern recognition (CVPR)

作者： Yong Yang Qiong Chen Yuan Feng Tianlin Huang School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5 i and COCO-20 i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet.

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Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning

Learning with Fantasy: Semantic-Aware Virtual Contrastive Co...

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conference on computer vision and pattern recognition (CVPR)

作者： Zeyin Song Yifan Zhao Yujun Shi Peixi Peng Li Yuan Yonghong Tian School of Electronic and Computer Engineering Peking University School of Computer Science Peking University National University of Singapore Peng Cheng Laboratory

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naïve supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space, but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://***/zysong0113/SAVC.

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CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation

CLIP is Also an Efficient Segmenter: A Text-Driven Approach ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuqi Lin Minghao Chen Wenxiao Wang Boxi Wu Ke Li Binbin Lin Haifeng Liu Xiaofei He State Key Lab of CAD&CG College of Computer Science Zhejiang University School of Software Technology Zhejiang University Fullong Technology

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Mean-while, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP- ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://***/linyq2117/CLIP-ES.

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