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Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-view Clustering

Highly Confident Local Structure Based Consensus Graph Learn...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jie Wen Chengliang Liu Gehui Xu Zhihao Wu Chao Huang Lunke Fei Yong Xu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China School of Computer Science and Technology Guangdong University of Technology Guangzhou China Pengcheng Laboratory Shenzhen China

Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness. Considering the reality of a large amount of incomplete data, in this paper, we propose a simple but effective method for incomplete multi-view clustering based on consensus graph learning, termed as HCLS_CGL. Unlike existing methods that utilize graph constructed from raw data to aid in the learning of consistent representation, our method directly learns a consensus graph across views for clustering. Specifically, we design a novel confidence graph and embed it to form a confidence structure driven consensus graph learning model. Our confidence graph is based on an intuitive similar-nearest-neighbor hypothesis, which does not require any additional information and can help the model to obtain a high-quality consensus graph for better clustering. Numerous experiments are performed to confirm the effectiveness of our method.

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BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

BAEFormer: Bi-Directional and Early Interaction Transformers...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Cong Pan Yonghao He Junran Peng Qian Zhang Wei Sui Zhaoxiang Zhang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Future Technology University of Chinese Academy of Sciences Horizon Robotics Huawei Inc. Center for Artificial Intelligence and Robotics HKISI_CAS

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multi-view image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.

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Transferable Adversarial Attacks on vision Transformers with Token Gradient Regularization

Transferable Adversarial Attacks on Vision Transformers with...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jianping Zhang Yizhan Huang Weibin Wu Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong School of Software Engineering Sun Yat-sen University

vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a local model to generate adversarial samples and directly transfer them to attack a target black-box model. The high efficiency of transfer-based attacks makes it a severe security threat to ViT-based applications. Therefore, it is vital to design effective transfer-based attacks to identify the deficiencies of ViTs beforehand in security-sensitive scenarios. Existing efforts generally focus on regularizing the input gradients to stabilize the updated direction of adversarial samples. However, the variance of the back-propagated gradients in intermediate blocks of ViTs may still be large, which may make the generated adversarial samples focus on some model-specific features and get stuck in poor local optima. To overcome the shortcomings of existing approaches, we propose the Token Gradient Regularization (TGR) method. According to the structural characteristics of ViTs, TGR reduces the variance of the back-propagated gradient in each internal block of ViTs in a token-wise manner and utilizes the regularized gradient to generate adversarial samples. Extensive experiments on attacking both ViTs and CNNs confirm the superiority of our approach. Notably, compared to the state-of-the-art transfer-based at-tacks, our TGR offers a performance improvement of 8.8% on average.

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A Large-Scale Homography Benchmark

A Large-Scale Homography Benchmark

引用

conference on computer vision and pattern recognition (CVPR)

作者： Daniel Barath Dmytro Mishkin Michal Polic Wolfgang Förstner Jiri Matas Computer Vision and Geometry Group ETH Zurich Switzerland VRG Faculty of Electrical Engineering CTU in Prague Czech Republic HOVER Inc. CIIRC CTU in Prague Czech Republic University Bonn Germany

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of the Pi3D dataset are diverse, e.g. training or evaluating monocular depth, surface normal estimation and image matching algorithms. The HEB dataset consists of 226 260 homographies and includes roughly 4M correspondences. The homographies link images that often undergo significant viewpoint and illumination changes. As applications of HEB, we perform a rigorous evaluation of a wide range of robust estimators and deep learning-based correspondence filtering methods, establishing the current state-of-the-art in robust homography estimation. We also evaluate the uncertainty of the SIFT orientations and scales w.r.t. the ground truth coming from the underlying homographies and provide codes for comparing uncertainty of custom detectors. The dataset is available at https://***/danini/homography-benchmark.

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Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Rethinking Image Super Resolution from Long-Tailed Distribut...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuanbiao Gou Peng Hu Jiancheng Lv Hongyuan Zhu Xi Peng College of Computer Science Sichuan University China Institute for Infocomm Research (I2R) A*STAR Singapore

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority.

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PVO: Panoptic Visual Odometry

PVO: Panoptic Visual Odometry

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conference on computer vision and pattern recognition (CVPR)

作者： Weicai Ye Xinyue Lan Shuo Chen Yuhang Ming Xingyuan Yu Hujun Bao Zhaopeng Cui Guofeng Zhang State Key Lab of CAD&CG Zhejiang University ZJU-SenseTime Joint Lab of 3D Vision School of Computer Science Hangzhou Dianzi University VIL University of Bristol

We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information. Our PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, which makes the two tasks mutually beneficial. Specifically, we introduce a panoptic update module into the VO Module with the guidance of image panoptic segmentation. This Panoptic-Enhanced VO Module can alleviate the impact of dynamic objects in the camera pose estimation with a panoptic-aware dynamic mask. On the other hand, the VO-Enhanced VPS Module also improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO Module. These two modules contribute to each other through recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks.

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Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lianyu Wang Meng Wang Daoqiang Zhang Huazhu Fu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks crossdomain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

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X-Pruner: eXplainable Pruning for vision Transformers

X-Pruner: eXplainable Pruning for Vision Transformers

引用

conference on computer vision and pattern recognition (CVPR)

作者： Lu Yu Wei Xiang James Cook University La Trobe University

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship between internal units of the model and the target class, thereby leading to inferior performance. To alleviate this problem, we propose a novel explainable pruning framework dubbed X-Pruner, which is designed by considering the explainability of the pruning criterion. Specifically, to measure each prunable unit's contribution to predicting each target class, a novel explainability-aware mask is proposed and learned in an end-to-end manner. Then, to preserve the most informative units and learn the layer-wise pruning rate, we adaptively search the layer-wise threshold that differentiates between unpruned and pruned units based on their explainability-aware mask values. To verify and evaluate our method, we apply the X-Pruner on representative transformer models including the DeiT and Swin Transformer. Comprehensive simulation results demonstrate that the proposed X-Pruner outperforms the state-of-the-art black-box methods with significantly reduced computational costs and slight performance degradation. Code is available at https://***/vickyyu90/XPruner.

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Yoga-82: A New Dataset for Fine-grained Classification of Human Poses

Yoga-82: A New Dataset for Fine-grained Classification of Hu...

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

作者： Verma, Manisha Kumawat, Sudhakar Nakashima, Yuta Raman, Shanmuganathan Osaka Univ Osaka Japan Indian Inst Technol Gandhinagar Gandhinagar India

ISBN: (纸本)9781728193601

Human pose estimation is a well-known problem in computer vision to locate joint positions. Existing datasets for learning of poses are observed to be not challenging enough in terms of pose diversity, object occlusion and view points. This makes the pose annotation process relatively simple and restricts the application of the models that have been trained on them. To handle more variety in human poses, we propose the concept of fine-grained hierarchical pose classification, in which we formulate the pose estimation as a classification task, and propose a dataset, Yoga-82, for large-scale yoga pose recognition with 82 classes. Yoga-82 consists of complex poses where fine annotations may not be possible. To resolve this, we provide hierarchical labels for yoga poses based on the body configuration of the pose. The dataset contains a three-level hierarchy including body positions, variations in body positions, and the actual pose names. We present the classification accuracy of the state-of-the-art convolutional neural network architectures on Yoga-82. We also present several hierarchical variants of DenseNet in order to utilize the hierarchical labels.

关键词： computer vision

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RDONet: Rate-Distortion Optimized Learned Image Compression with Variable Depth

RDONet: Rate-Distortion Optimized Learned Image Compression ...

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

作者： Fabian Brand Kristian Fischer Alexander Kopte Marc Windsheimer André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universit&#x00E4 t Erlangen-N&#x00FC rnberg Erlangen Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Rate-distortion optimization (RDO) is responsible for large gains in image and video compression. While RDO is a standard tool in traditional image and video coding, it is not yet widely used in novel end-to-end trained neural methods. The major reason is that the decoding function is trained once and does not have free parameters. In this paper, we present RDONet, a network containing state-of-the-art components, which is perceptually optimized and capable of rate-distortion optimization. With this network, we are able to outperform VVC Intra on MS-SSIM and two different perceptual LPIPS metrics. This paper is part of the CLIC challenge, where we participate under the team name RDONet FAU.

关键词： Measurement Video coding Training Image coding Rate-distortion Video compression pattern recognition

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