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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4901-4910 订阅

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Boosting Adversarial Transferability by Block Shuffle and Rotation

Boosting Adversarial Transferability by Block Shuffle and Ro...

引用

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

作者： Wang, Kunyu He, Xuanran Wang, Wenxuan Wang, Xiaosen Chinese Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore Huawei Singular Secur Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding, we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically, BSR splits the input image into several blocks, then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability, which significantly outperforms the state-of-the-art methods. Code is available at https://***/Trustworthy-AI-Group/BSR.

关键词： adversarial attack computer vision

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Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification

Joint Distribution Matters: Deep Brownian Distance Covarianc...

引用

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

作者： Xie, Jiangtao Long, Fei Lv, Jiaming Wang, Qilong Li, Peihua Dalian Univ Technol Dalian Peoples R China Tianjin Univ Tianjin Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://***/DeepBDC.

关键词： Training Deep learning computer vision Transfer learning Image representation Extraterrestrial measurements pattern recognition

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Creating invariance to "nuisance parameters" in face recognition

Creating invariance to "nuisance parameters" in face recogni...

引用

2005 ieee computer society conference on computer vision and pattern recognition, CVPR 2005

作者： Prince, Simon J. D. Elder, James H. York University Centre for Vision Research Toronto Ont. Canada

ISBN: (纸本)0769523722

A major goal for face recognition is to identify faces where the pose of the probe is different from the stored face. Typical feature vectors vary more with pose than with identity, leading to very poor recognition performance. We propose a non-linear many-to-one mapping from a conventional feature space to a new space constructed so that each individual has a unique feature vector regardless of pose. Training data is used to implicitly parameterize the position of the multi-dimensional face manifold by pose. We introduce a co-ordinate transform which depends on the position on the manifold. This transform is chosen so that different poses of the same face are mapped to the same feature vector. The same approach is applied to illumination changes. We investigate different methods for creating features which are invariant to both pose and illumination. We provide a metric to assess the discriminability of the resulting features. Our technique increases the discriminability of faces under unknown pose and lighting compared to contemporary methods.

关键词： Face recognition

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OCSampler: Compressing Videos to One Clip with Single-step Sampling

OCSampler: Compressing Videos to One Clip with Single-step S...

引用

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

作者： Lin, Jintao Duan, Haodong Chen, Kai Lin, Dahua Wang, Limin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Videos incorporate rich semantics as well as redundant information. Seeking a compact yet effective video representation, e.g., sample informative frames from the entire video, is critical to efficient video recognition. There have been works that formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance. In this paper, we present a more efficient framework named OCSampler, which explores such a representation with one short clip. OCSampler designs a new paradigm of learning instance-specific video condensation policies to select frames only in a single step. Rather than picking up frames sequentially like previous methods, we simply process a whole sequence at once. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high confidence with as few frames as possible. Experiments on various benchmarks demonstrate the effectiveness of OCSampler over previous methods in terms of accuracy and efficiency. Specifically, it achieves 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU.

关键词： computer vision Semantics Graphics processing units Benchmark testing Throughput Sampling methods pattern recognition

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Towards Robust and Reproducible Active Learning using Neural Networks

Towards Robust and Reproducible Active Learning using Neural...

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

作者： Munjal, Prateek Hayat, Nasir Hayat, Munawar Sourati, Jamshid Khan, Shadab G42 Healthcare Abu Dhabi U Arab Emirates NYUAD Abu Dhabi U Arab Emirates Monash Univ Clayton Vic Australia Univ Chicago Chicago IL 60637 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental settings, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously reported results. We also found that under strong regularization, AL methods show marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL evaluations. We believe our findings and recommendations will help advance reproducible research in AL using neural networks.

关键词： Measurement computer vision Uncertainty Costs Codes Annotations Neural networks

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Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking

Learning Dynamics via Graph Neural Networks for Human Pose E...

引用

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

作者： Yang, Yiding Ren, Zhou Li, Haoxiang Zhou, Chunluan Wang, Xinchao Hua, Gang Stevens Inst Technol Hoboken NJ 07030 USA Wormpex AI Res Bellevue WA USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

Multi person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detections especially in heavily-cluttered scenes, since this tracking-by-detection paradigm is, by nature, largely dependent on visual evidences that are absent in the case of occlusion. In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion. Specifically, we derive this prediction of dynamics through a graph neural network (GNN) that explicitly accounts for both spatial-temporal and visual information. It takes as input the historical pose tracklets and directly predicts the corresponding poses in the following frame for each tracklet. The predicted poses will then be aggregated with the detected poses, if any, at the same frame so as to produce the final pose, potentially recovering the occluded joints missed by the estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.

关键词： Visualization computer vision Aggregates Pose estimation Pipelines Predictive models Graph neural networks

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Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

Synthesizing Long-Term 3D Human Motion and Interaction in 3D...

引用

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

作者： Wang, Jiashun Xu, Huazhe Xu, Jingwei Liu, Sifei Wang, Xiaolong Univ Calif San Diego San Diego CA 92093 USA Univ Calif Berkeley Berkeley CA USA Shanghai Jiao Tong Univ Shanghai Peoples R China NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure. Building on this framework, we further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.

关键词： Graphics Geometry computer vision Three-dimensional displays Affordances Computational modeling Cognition

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Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

Anti-aliasing Semantic Reconstruction for Few-Shot Semantic ...

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

作者： Liu, Binghao Ding, Yao Jiao, Jianbin Ji, Xiangyang Ye, Qixiang Univ Chinese Acad Sci EECE PriSDL Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781665445092

Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples. However, this feature sharing mechanism inevitably causes semantic aliasing between novel classes when they have similar compositions of semantic concepts. In this paper, we reformulate few-shot segmentation as a semantic reconstruction problem, and convert base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction. By introducing contrastive loss, we maximize the orthogonality of basis vectors while minimizing semantic aliasing between classes. Within the reconstructed representation space, we further suppress interference from other classes by projecting query features to the support vector for precise semantic activation. Our proposed approach, referred to as anti-aliasing semantic reconstruction (ASR), provides a systematic yet interpretable solution for few-shot learning problems. Extensive experiments on PASCAL VOC and MS COCO datasets show that ASR achieves strong results compared with the prior works. Code will be released at ***/Bibkiller/ASR.

关键词： Training Support vector machines computer vision Systematics Codes Semantics Training data

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HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images

HODOR: High-level Object Descriptors for Object Re-segmentat...

引用

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

作者： Athar, Ali Luiten, Jonathon Hermans, Alexander Ramanan, Deva Leibe, Bastian Rhein Westfal TH Aachen Aachen Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. With out any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. Source code: https://***/Ali2500/HODOR.

关键词： Training computer vision Image coding Codes Annotations Image annotation Object segmentation

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GeneCIS: A Benchmark for General Conditional Image Similarity

GeneCIS: A Benchmark for General Conditional Image Similarit...

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

作者： Vaze, Sagar Carion, Nicolas Misra, Ishan Meta AI FAIR Menlo Pk CA 94025 USA Univ Oxford VGG Oxford England

ISBN: (纸本)9798350301298

We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States.

关键词： language reasoning vision

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