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Learning An Explicit Weighting Scheme for Adapting Complex HSI Noise

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

引用

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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Learning the viewpoint manifold for action recognition

Learning the viewpoint manifold for action recognition

引用

ieee conference on computer vision and pattern recognition

作者： Souvenir, Richard Babbs, Justin Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA

ISBN: (纸本)9781424422425

Researchers are increasingly interested in providing video-based, view-invariant action recognition for human motion. Addressing this problem will lead to more accurate modeling and analysis of the type of unconstrained video commonly collected in the areas of athletics and medicine. Previous viewpoint-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available data set previously only acheieved using multiple simultaneous views.

关键词： Cameras

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Strong-Weak Distribution Alignment for Adaptive Object Detection 32

Strong-Weak Distribution Alignment for Adaptive Object Detec...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Saito, Kuniaki Ushiku, Yoshitaka Harada, Tatsuya Saenko, Kate Boston Univ Boston MA 02215 USA Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (纸本)9781728132938

We propose an approachfor unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection. Recently, approaches that align distributions of source and target images using an adversarial loss have been proven effective for adapting object classifiers. However for object detection,fully matching the entire distributions of source and target images to each other at the global image level may fail, as domains could have distinct scene layouts and different combinations of objects. On the other hand, strong matching of local features such as texture and color makes sense, as it does not change category level semantics. This motivates us to propose a novel method for detectorad aptation based on strong local alignment and weak global alignment. Our key contribution is the weak alignment model, which focuses the adversarial alignment loss on images that are globally similar and puts less emphasis on aligning images that are globally dissimilar Additionally, we design the strong domain alignment model to only look at local receptive fields of the feature map. We empirically verify the effectiveness of our method on four datasets comprising both large and small domain shifts.

关键词： Categorization Deep Learning recognition: Detection Representation Learning Retrieval

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Layer-wise Searching for 1-bit Detectors

Layer-wise Searching for 1-bit Detectors

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

作者： Xu, Sheng Zhao, Junhe Lu, Jinhu Zhang, Baochang Han, Shumin Doermann, David Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Shenzhen Acad Aerosp Technol Shenzhen Peoples R China Baidu Inc Dept Comp Vis Technol Beijing Peoples R China SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781665445092

1-bit detectors show great promise for resource-constrained embedded devices but often suffer from a significant performance gap compared with their real-valued counterparts. The primary reason lies in the error during binarization. This paper presents a layer-wise searching (LWS) strategy to generate 1-bit detectors that maintain a performance very close to the original real-valued model. The approach introduces angular and amplitude loss functions to increase detector capacity. At 1-bit layers, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework. We also learn the scale factor by minimizing the amplitude loss in the same student-teacher framework. Extensive experiments show that LWS-Det outperforms state-of-the-art 1-bit detectors by a considerable margin on the PASCAL VOC and COCO datasets. For example, the LWS-Det achieves 1-bit Faster-RCNN with ResNet-34 backbone within 2.0% mAP of its real-valued counterpart on the PASCAL VOC dataset.

关键词： Performance evaluation Training computer vision Satellite broadcasting Detectors Object detection pattern recognition

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StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retriev...

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

作者： Sain, Aneeshan Bhunia, Ayan Kumar Yang, Yongxin Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England

ISBN: (纸本)9781665445092

Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn by humans and considerable style variations exist amongst different users. An effective SBIR model needs to explicitly account for this style diversity, crucially, to generalise to unseen user styles. To this end, a novel style-agnostic SBIR model is proposed. Different from existing models, a cross-modal variational autoencoder (VAE) is employed to explicitly disentangle each sketch into a semantic content part shared with the corresponding photo, and a style part unique to the sketcher. Importantly, to make our model dynamically adaptable to any unseen user styles, we propose to metatrain our cross-modal VAE by adding two style-adaptive components: a set of feature transformation layers to its encoder and a regulariser to the disentangled semantic content latent code. With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic. Extensive experiments show that our style-agnostic model yields state-of-the-art performance for both category-level and instance-level SBIR.

关键词： Adaptation models computer vision Codes Computational modeling Semantics Image retrieval pattern recognition

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See through Gradients: Image Batch Recovery via GradInversion

See through Gradients: Image Batch Recovery via GradInversio...

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

作者： Yin, Hongxu Mallya, Arun Vahdat, Arash Alvarez, Jose M. Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781665445092

Training deep neural networks requires gradient estimation from data batches to update parameters. Gradients per parameter are averaged over a set of data and this has been presumed to be safe for privacy-preserving training in joint, collaborative, and federated learning applications. Prior work only showed the possibility of recovering input data given gradients under very restrictive conditions - a single input point, or a network with no non-linearities, or a small 32 x 32 px input batch. Therefore, averaging gradients over larger batches was thought to be safe. In this work, we introduce GradInversion, using which input images from a larger batch (8 - 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224 x 224 px). We formulate an optimization task that converts random noise into natural images, matching gradients while regularizing image fidelity. We also propose an algorithm for target class label recovery given gradients. We further propose a group consistency regularization framework, where multiple agents starting from different random seeds work together to find an enhanced reconstruction of the original data batch. We show that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes.

关键词： Training Deep learning Data privacy Image matching Estimation Collaborative work pattern recognition

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Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation

Practical Stereo Matching via Cascaded Recurrent Network wit...

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

作者： Li, Jiankun Wang, Peisen Xiong, Pengfei Cai, Tao Yan, Ziwei Yang, Lei Liu, Jiangyu Fan, Haoqiang Liu, Shuaicheng Megvii Res Beijing Peoples R China Tencent Shenzhen Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference;2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification;3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions.

关键词： Correlation Adaptive systems Training data Benchmark testing Network architecture Real-time systems pattern recognition

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Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation

Rethinking Depth Estimation for Multi-View Stereo: A Unified...

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

作者： Peng, Rui Wang, Rongjie Wang, Zhenyu Lai, Yawen Wang, Ronggang Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods. Although these two representations have recently demonstrated their excellent performance, they still have apparent shortcomings, e.g., regression methods tend to overfit due to the indirect learning cost volume, and classification methods cannot directly infer the exact depth due to its discrete prediction. In this paper, we propose a novel representation, termed Unification, to unify the advantages of regression and classification. It can directly constrain the cost volume like classification methods, but also realize the sub-pixel depth prediction like regression methods. To excavate the potential of unification, we design a new loss function named Unified Focal Loss, which is more uniform and reasonable to combat the challenge of sample imbalance. Combining these two unburdened modules, we present a coarse-to-fine framework, that we call UniMVSNet. The results of ranking first on both DTU and Tanks and Temples benchmarks verify that our model not only performs the best but also has the best generalization ability.

关键词： computer vision Costs Three-dimensional displays Image analysis Estimation Benchmark testing Sensors

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vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments 31

Vision-and-Language Navigation: Interpreting visually-ground...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Anderson, Peter Wu, Qi Teney, Damien Bruce, Jake Johnson, Mark Sunderhauf, Niko Reid, Ian Gould, Stephen van den Hengel, Anton Australian Natl Univ Canberra ACT Australia Univ Adelaide Adelaide SA Australia Queensland Univ Technol Brisbane Qld Australia Macquarie Univ N Ryde NSW Australia

ISBN: (纸本)9781538664209

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery left perpendicularI Iright perpendicular. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset

关键词： Navigation Task analysis Robots Visualization Cameras Three-dimensional displays Natural languages

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Facial expression recognition using encoded dynamic features

Facial expression recognition using encoded dynamic features

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ieee conference on computer vision and pattern recognition

作者： Yang, Peng Liu, Qingshan Cui, Xinyi Metaxas, Dimitris N. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9781424422425

In this paper, we propose a novel framework for video-based facial expression recognition, which can handle the data with various time resolution including a single frame. We first use the haar-like features to represent facial appearance, due to their simplicity and effectiveness. Then we perform K-Means clustering on the facial appearance features to explore the intrinsic temporal patterns of each expression. Based on the temporal pattern models, we further map the facial appearance variations into dynamic binary patterns. Finally, boosting learning is performed to construct the expression classifiers. Compared to previous work, the dynamic binary patterns encode the intrinsic dynamics of expression, and our method makes no assumption on the time resolution of the data. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.

关键词： Face recognition

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