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Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association

Seeking the Shape of Sound: An Adaptive Framework for Learni...

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

作者： Wen, Peisong Xu, Qianqian Jiang, Yangbangyan Yang, Zhiyong He, Yuan Huang, Qingming Chinese Acad Sci Inst Comput Tech Key Lab Intel Info Proc Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Tech Beijing Peoples R China Chinese Acad Sci Inst Info Engn State Key Lab Info Secur SKLOIS Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Alibaba Grp Beijing Peoples R China Univ Chinese Acad Sci BDKM Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.

关键词： computer vision Matched filters Art Shape Filtering Face recognition pattern matching

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MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network

MR Image Super-Resolution with Squeeze and Excitation Reason...

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

作者： Zhang, Yulun Li, Kai Li, Kunpeng Fu, Yun Northeastern Univ Dept ECE Boston MA 02115 USA Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9781665445092

High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usually share some visual characteristics: repeating patterns, relatively simpler structures, and less informative background. Most previous CNN-based SR methods treat the spatial pixels (including the background) equally. They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors. Such global descriptors enhance the network's ability to focus on more informative regions and structures in MR images. We further build relationship among those global descriptors and propose primitive relationship reasoning attention. The global descriptors are further refined with learned attention. To fully make use of the aggregated information, we adaptively recalibrate feature responses with learned adaptive attention vectors. These attention vectors select a subset of global descriptors to complement each spatial location for accurate details and texture reconstruction. We propose squeeze and excitation attention with residual scaling, which not only stabilizes the training but also makes it flexible to other basic networks. Extensive experiments show the effectiveness of our proposed SERAN, which clearly surpasses state-of-the-art methods on benchmarks quantitatively and visually.

关键词： Training Visualization computer vision Adaptation models Computational modeling Superresolution Cognition

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Recognizing Rigid patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives

Recognizing Rigid Patterns of Unlabeled Point Clouds by Comp...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Daniel Widdowson Vitaliy Kurlin Computer Science department University of Liverpool UK

Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points. The most natural equivalence on these point clouds is rigid motion or isometry maintaining all inter-point distances. Rigid patterns of point clouds can be reliably compared only by complete isometry invariants that can also be called equivariant descriptors without false negatives (isometric clouds having different descriptions) and without false positives (non-isometric clouds with the same description). Noise and motion in data motivate a search for invariants that are continuous under perturbations of points in a suitable metric. We propose the first continuous and complete invariant of unlabeled clouds in any Euclidean space. For a fixed dimension, the new metric for this invariant is computable in a polynomial time in the number of points.

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Multi-Stage Progressive Image Restoration

Multi-Stage Progressive Image Restoration

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

作者： Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Yang, Ming-Hsuan Shao, Ling Incept Inst AI Abu Dhabi U Arab Emirates Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Monash Univ Clayton Vic Australia Univ Calif Merced Merced CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a twofaceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://***/swz30/MPRNet.

关键词： Runtime Computational modeling Noise reduction computer architecture Performance gain Image restoration pattern recognition

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Improving OCR-based Image Captioning by Incorporating Geometrical Relationship

Improving OCR-based Image Captioning by Incorporating Geomet...

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

作者： Wang, Jing Tang, Jinhui Yang, Mingkun Bai, Xiang Luo, Jiebo Nanjing Univ Sci & Technol Nanjing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OCR tokens. Existing methods attempt to achieve this goal via encoding the OCR tokens with rich visual and semantic representations. However, strong correlations between OCR tokens may not be established with such limited representations. In this paper, we propose to enhance the connections between OCR tokens from the viewpoint of exploiting the geometrical relationship. We comprehensively consider the height, width, distance, IoU and orientation relations between the OCR tokens for constructing the geometrical relationship. To integrate the learned relation as well as the visual and semantic representations into a unified framework, a Long Short-Term Memory plus Relation-aware pointer network (LSTM-R) architecture is presented in this paper. Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning. Extensive experiments demonstrate the effectiveness of our LSTM-R. More remarkably, LSTM-R achieves state-of-the-art performance on TextCaps, with the CIDEr-D score being increased from 98.0% to 109.3%.

关键词： Training Visualization computer vision Correlation Computational modeling Semantics computer architecture

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Boosting Video Representation Learning with Multi-Faceted Integration

Boosting Video Representation Learning with Multi-Faceted In...

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Zhang, Xiao-Ping Wu, Dong Mei, Tao JD AI Res Beijing Peoples R China Singapore Management Univ Singapore Singapore Ryerson Univ Toronto ON Canada

ISBN: (纸本)9781665445092

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The prelearnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.

关键词： Training computer vision Three-dimensional displays Aggregates Semantics Feature extraction Boosting

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IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

IoU Attack: Towards Temporally Coherent Black-Box Adversaria...

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

作者： Jia, Shuai Song, Yibing Ma, Chao Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781665445092

Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers. Assuming that the model structures of deep trackers are known, a variety of white-box attack approaches to visual tracking have demonstrated promising results. However, the model knowledge about deep trackers is usually unavailable in real applications. In this paper, we propose a decision-based black-box attack method for visual object tracking. In contrast to existing black-box adversarial attack methods that deal with static images for image classification, we propose IoU attack that sequentially generates perturbations based on the predicted IoU scores from both current and historical frames. By decreasing the IoU scores, the proposed attack method degrades the accuracy of temporal coherent bounding boxes (i.e., object motions) accordingly. In addition, we transfer the learned perturbations to the next few frames to initialize temporal motion attack. We validate the proposed IoU attack on state-of-the-art deep trackers (i.e., detection based, correlation filter based, and long-term trackers). Extensive experiments on the benchmark datasets indicate the effectiveness of the proposed IoU attack method. The source code is available at https://***/vision-SJTU/IoUattack.

关键词： Deep learning Visualization computer vision Correlation Codes Perturbation methods Robustness

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Diverse Branch Block: Building a Convolution as an Inception-like Unit

Diverse Branch Block: Building a Convolution as an Inception...

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

作者： Ding, Xiaohan Zhang, Xiangyu Han, Jungong Ding, Guiguang Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China MEGVII Technol Beijing Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth SY23 3FL Dyfed Wales MEGVII Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multi-scale convolutions, and average pooling. After training, a DBB can be equivalently converted into a single conv layer for deployment. Unlike the advancements of novel ConvNet architectures, DBB complicates the training-time microstructure while maintaining the macro architecture, so that it can be used as a drop-in replacement for regular conv layers of any architecture. In this way, the model can be trained to reach a higher level of performance and then transformed into the original inference-time structure for inference. DBB improves ConvNets on image classification (up to 1.9% higher top-1 accuracy on ImageNet), object detection and semantic segmentation. The PyTorch code and models are released at https:// ***/ DingXiaoH/DiverseBranchBlock.

关键词： Training Image segmentation Costs Convolution Semantics computer architecture Object detection

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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

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conference on computer vision and pattern recognition (cvpr)

作者： Shahira Abousamra Rajarsi Gupta Tahsin Kurc Dimitris Samaras Joel Saltz Chao Chen Department of Computer Science Stony Brook University USA Department of Biomedical Informatics Stony Brook University USA

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

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Interpreting Super-Resolution Networks with Local Attribution Maps

Interpreting Super-Resolution Networks with Local Attributio...

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

作者： Gu, Jinjin Dong, Chao Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Image super-resolution (SR) techniques have been developing rapidly, benefiting from the invention of deep networks and its successive breakthroughs. However, it is acknowledged that deep learning and deep neural networks are difficult to interpret. SR networks inherit this mysterious nature and little works make attempt to understand them. In this paper, we perform attribution analysis of SR networks, which aims at finding the input pixels that strongly influence the SR results. We propose a novel attribution approach called local attribution map (LAM), which inherits the integral gradient method yet with two unique features. One is to use the blurred image as the baseline input, and the other is to adopt the progressive blurring function as the path function. Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance. (2) Attention networks and non-local networks extract features from a wider range of input pixels. (3) Comparing with the range that actually contributes, the receptive field is large enough for most deep networks. (4) For SR networks, textures with regular stripes or grids are more likely to be noticed, while complex semantics are difficult to utilize. Our work opens new directions for designing SR networks and interpreting low-level vision deep models.

关键词： Deep learning Visualization Technological innovation Gradient methods computer vision Superresolution Semantics

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