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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise

Learning An Explicit Weighting Scheme for Adapting Complex H...

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

作者： Rui, Xiangyu Cao, Xiangyong Xie, Qi Yue, Zongsheng Zhao, Qian Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665445092

An efficient approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements. Such weighting scheme, however, largely depends on the prior understanding or subjective distribution assumption on HSI noises, making them easily biased to complicated real noises, and hardly generalizable to diverse practical scenarios. Against this issue, this paper proposes a new scheme aiming to capture general weighting principle in a data-driven manner. Specifically, such weighting principle is delivered by an explicit function, called hyperweight-net (HWnet), mapping from an input noisy image to its properly imposed weights. A Bayesian framework as well as a variational inference algorithm for inferring HWnet parameters is elaborately designed, expecting to extract the latent weighting rule for general diverse and complicated noisy HSIs. Comprehensive experiments substantiate that the learned HWnet can be not only finely generalized to different noise types from those used in training, but also effectively transferred to other weighted models. Besides, as a sounder guidance, HWnet can help to more faithfully and robustly achieve deep hyperspectral prior(DHP). The extracted weights by HWnet are verified to be able to effectively capture complex noise knowledge underlying input HSI, revealing its working insight in experiments.

关键词： Training Adaptation models computer vision Computational modeling Noise reduction Inference algorithms pattern recognition

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Optimal Correction Cost for Object Detection Evaluation

Optimal Correction Cost for Object Detection Evaluation

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

作者： Otani, Mayu Togashi, Riku Nakashima, Yuta Rahtu, Esa Heikkila, Janne Satoh, Shin'ichi CyberAgent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms of the performance of ranked instance retrieval. Such the assumption for the evaluation task does not suit some downstream tasks. To alleviate the gap between downstream tasks and the evaluation scenario, we propose Optimal Correction Cost (OC-cost), which assesses detection accuracy at image level. OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy. The cost is obtained by solving an optimal transportation problem between the detections and the ground truths. Unlike mAP, OC-cost is designed to penalize false positive and false negative detections properly, and every image in a dataset is treated equally. Our experimental result validates that OC-cost has better agreement with human preference than a ranking-based measure, i.e., mAP for a single image. We also show that detectors' rankings by OC-cost are more consistent on different data splits than mAP. Our goal is not to replace mAP with OC-cost but provide an additional tool to evaluate detectors from another aspect. To help future researchers and developers choose a target measure, we provide a series of experiments to clarify how mAP and OC-cost differ.

关键词： computer vision Costs Layout Transportation Detectors Object detection pattern recognition

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Efficient Classification of Very Large Images with Tiny Objects

Efficient Classification of Very Large Images with Tiny Obje...

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

作者： Kong, Fanjie Henao, Ricardo Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify , very large images with tiny informative objects. Specifically, these classification tasks face two key challenges: i) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images: and ii) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (R01) to image ratio. However, most of the current convolutional neural networks (CNNs) are designed for image classification datasets that have relatively large ROIs and small image sizes (sub-megapixel). Existing approaches have addressed these two challenges in isolation. We present an end-to-end CNN model termed Zoom-In network that leverages hierarchical attention sampling for classification of large images with tiny objects using a single GPU. We evaluate our method on four large-image histopathology, road-scene and satellite imaging datasets, and one gigapixel pathology dataset. Experimental results show that our model achieves higher accuracy than existing methods while requiring less memory resources.

关键词： computer vision Satellites Microscopy Microprocessors Machine vision Memory management Convolutional neural networks

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Transitional Adaptation of Pretrained Models for Visual Storytelling

Transitional Adaptation of Pretrained Models for Visual Stor...

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Kim, Jongseok Kim, Gunhee Allen Inst AI Seattle WA 98103 USA Seoul Natl Univ Seoul South Korea Violet Seoul South Korea

ISBN: (纸本)9781665445092

Previous models for vision-to-language generation tasks usually pretrain a visual encoder and a language generator in the respective domains and jointly finetune them with the target task. However, this direct transfer practice may suffer from the discord between visual specificity and language fluency since they are often separately trained from large corpora of visual and text data with no common ground. In this work, we claim that a transitional adaptation task is required between pretraining and finetuning to harmonize the visual encoder and the language model for challenging downstream target tasks like visual storytelling. We propose a novel approach named Transitional Adaptation of Pre-trained Model (TAPM) that adapts the multi-modal modules to each other with a simpler alignment task between visual inputs only with no need for text labels. Through extensive experiments, we show that the adaptation step significantly improves the performance of multiple language models for sequential video and image captioning tasks. We achieve new state-of-the-art performance on both language metrics and human evaluation in the multi-sentence description task of LSMDC 2019 [50] and the image storytelling task of VIST [18]. Our experiments reveal that this improvement in caption quality does not depend on the specific choice of language models.

关键词： Measurement Visualization Adaptation models computer vision Computational modeling Generators pattern recognition

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Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

Shadows can be Dangerous: Stealthy and Effective Physical-wo...

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

作者： Zhong, Yiqi Liu, Xianming Zhai, Deming Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack.

关键词： Printing Laser theory computer vision Codes Perturbation methods Machine vision Machine learning

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COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

COOPERNAUT: End-to-End Driving with Cooperative Perception f...

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

作者： Cui, Jiaxun Qiu, Hang Chen, Dian Stone, Peter Zhu, Yuke Univ Texas Austin Austin TX 78712 USA Stanford Univ Stanford CA 94305 USA Sony AI Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hindered by the limited line-of-sight sensing capability and the brittleness of data-driven methods in handling extreme situations. With recent developments of telecommunication technologies, cooperative perception with vehicle-to-vehicle communications has become a promising paradigm to enhance autonomous driving in dangerous or emergency situations. We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels. To evaluate our model, we develop AUTOCASTSIM, a network-augmented driving simulation framework with example accident-prone scenarios. Our experiments on AUTOCASTSIM suggest that our cooperative perception driving models lead to a 40% improvement in average success rate over egocentric driving models in these challenging driving situations and a 5x smaller bandwidth requirement than prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at https://***/Coopernaut/.

关键词： Wireless communication Wireless sensor networks computer vision Ultraviolet sources Computational modeling Vehicular ad hoc networks pattern recognition

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Efficient Joint 2D and 3D Palmprint Matching with Alignment Refinement

Efficient Joint 2D and 3D Palmprint Matching with Alignment ...

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23rd IEEE conference on computer vision and pattern recognition (CVPR)

作者： Li, Wei Zhang, Lei Zhang, David Lu, Guangming Yan, Jingqi Hong Kong Polytech Univ Biometr Res Ctr Dept Comp Hong Kong Hong Kong Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recogni Shanghai 200030 Peoples R China Harbin Inst Technol Grad Sch Biocomp Res Ctr Shen Zhen Peoples R China

ISBN: (纸本)9781424469840

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.

关键词： Anthropometry

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OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer

OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real...

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

作者： Gao, Ruohan Si, Zilin Chang, Yen-Yu Clarke, Samuel Bohg, Jeannette Li Fei-Fei Yuan, Wenzhen Wu, Jiajun Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. OBJECTFOLDER 1.0 is a recent dataset that introduces 100 virtualized objects with visual, acoustic, and tactile sensory data. However;the dataset is small in scale and the multisensory data is of limited quality, hampering generalization to real-world scenarios. We present OBJECTFOLDER 2.0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances OBJECTFOLDER 1.0 in three aspects. First, our dataset is 10 times larger in the amount of objects and orders of magnitude faster in rendering time. Second, we significantly improve the multisensory rendering quality for all three modalities. Third, we show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts in three challenging tasks: object scale estimation, contact localization, and shape reconstruction. OBJECTFOLDER 2.0 offers a new path and testbed for multisensory learning in computer vision and robotics. The dataset is available at https: //***/rhgao/ObjectFolder.

关键词： Location awareness computer vision Visualization Shape Computational modeling Estimation Rendering (computer graphics)

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Globally Optimal Line Clustering and Vanishing Point Estimation in Manhattan World

Globally Optimal Line Clustering and Vanishing Point Estimat...

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IEEE conference on computer vision and pattern recognition (CVPR)

作者： Bazin, Jean-Charles Seo, Yongduek Demonceaux, Cedric Vasseur, Pascal Ikeuchi, Katsushi Kweon, Inso Pollefeys, Marc ETH CGL Zurich Switzerland Sogang Univ Dept Media Technol Sogang South Korea Univ Burgundy Le2i Burgundy France Univ Rouen LITIS Rouen France Univ Tokyo CV Lab Tokyo Japan Korea Adv Inst Sci & Technol RCV Lab Daejeon South Korea CVG ETHZ Zurich Switzerland

ISBN: (纸本)9781467312288

The projections of world parallel lines in an image intersect at a single point called the vanishing point (VP). VPs are a key ingredient for various vision tasks including rotation estimation and 3D reconstruction. Urban environments generally exhibit some dominant orthogonal VPs. Given a set of lines extracted from a calibrated image, this paper aims to (1) determine the line clustering, i.e. find which line belongs to which VP, and (2) estimate the associated orthogonal VPs. None of the existing methods is fully satisfactory because of the inherent difficulties of the problem, such as the local minima and the chicken-and-egg aspect. In this paper, we present a new algorithm that solves the problem in a mathematically guaranteed globally optimal manner and can inherently enforce the VP orthogonality. Specifically, we formulate the task as a consensus set maximization problem over the rotation search space, and further solve it efficiently by a branch-and-bound procedure based on the Interval Analysis theory. Our algorithm has been validated successfully on sets of challenging real images as well as synthetic data sets.

关键词： computer vision

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Mobile-Former: Bridging MobileNet and Transformer

Mobile-Former: Bridging MobileNet and Transformer

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

作者： Chen, Yinpeng Dai, Xiyang Chen, Dongdong Liu, Mengchen Dong, Xiaoyi Yuan, Lu Liu, Zicheng Microsoft Corp Redmond WA 98052 USA Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between. This structure leverages the advantages of MobileNet at local processing and transformer at global interaction. And the bridge enables bidirectional fusion of local and global features. Different from recent works on vision transformer, the transformer in Mobile-Former contains very few tokens (e.g. 6 or fewer tokens) that are randomly initialized to learn global priors, resulting in low computational cost. Combining with the proposed light-weight cross attention to model the bridge, Mobile-Former is not only computationally efficient, but also has more representation power. It outperforms MobileNetV3 at low FLOP regime from 25M to 500M FLOPs on ImageNet classification. For instance, Mobile-Former achieves 77.9% top-1 accuracy at 294M FLOPs, gaining 1.3% over MobileNetV3 but saving 17% of computations. When transferring to object detection, Mobile-Former outperforms MobileNetV3 by 8.6 AP in RetinaNet framework. Furthermore, we build an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR by 1.3 AP but saves 52% of computational cost and 36% of parameters. Code will be released athttps://***/aaboys/mobileformer.

关键词： Bridges computer vision Object detection Detectors Transformers Encoding Computational efficiency

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