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Cross-view Aggregation Network For Stereo Image Super-Resolution

Cross-view Aggregation Network For Stereo Image Super-Resolu...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Zhitao Chen Tao Lu Kanghui Zhao Bolin Zhu Zhen Li Jiaming Wang Yanduo Zhang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although stereo image super-resolution has been extensively studied, many existing works only rely on attention in a single epipolar direction to reconstruct stereo images. In the case of asymmetric parallax images, these methods often struggle to capture reliable stereo correspondence, resulting in reconstructed images suffering from blurring and artifacts. In this paper, we propose a novel method called Cross-View Aggregation Network for Stereo Image Super-Resolution (CANSSR) and explore the relationship between multi-directional epipolar lines to construct reliable stereo correspondence. Specifically, we propose a multidirectional cross-view aggregation module (MCAM) that effectively captures multi-directional stereo correspondence and obtains cross-view complementary information. Furthermore, we design a channel-spatial aggregation module (CSAM) that aggregates multi-order global-local information in intra-view to reconstruct clearer texture features. In addition, we equip a large kernel convolution in the Feedforward Network to acquire richer detailed texture information. The extensive experiments conclusively demonstrate that CANSSR outperforms the state-of-the-art method both qualitatively and quantitatively in terms of stereo image super-resolution on the Flickr 1024 and Middlebury datasets.

关键词： Image databases Convolution computer network reliability Superresolution pattern recognition Reliability Web sites

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Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent Units

Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Manon Dampfhoffer Thomas Mesquida CEA List Univ. Grenoble Alpes Grenoble France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic Lip-Reading (ALR) requires the recognition of spoken words based on a visual recording of the speaker’s lips, without access to the sound. ALR with neuromorphic event-based vision sensors, instead of traditional frame-based cameras, is particularly promising for edge applications due to their high temporal resolution, low power consumption and robustness. Neuromorphic models, such as Spiking Neural Networks (SNNs), encode information using events and are naturally compatible with such data. The sparse and event-based nature of both the sensor data and SNN activations can be leveraged in an end-to-end neuromorphic hardware pipeline for low-power and low-latency edge applications. However, the accuracy of SNNs is often largely degraded compared to state-of-the-art non-spiking Artificial Neural Networks (ANNs). In this work, a new SNN model, the Signed Spiking Gated Recurrent Unit (SpikGRU2+), is proposed and used as a task head for event-based ALR. The SNN architecture is as accurate as its ANN equivalent, and outperforms the state-of-the-art on the DVS-Lip dataset. Notably, the accuracy is improved by 25% (respectively 4%) compared to the previous state-of-the-art SNN (respectively ANN). In addition, the SNN spike sparsity can be optimized to further reduce the number of operations up to 22x compared to the ANN while maintaining a high accuracy. This work opens up new perspectives for the use of SNNs for accurate and low-power end-to-end neuromorphic gesture recognition. Code is available 1 .

关键词： Visualization Accuracy Neuromorphics Gesture recognition Spiking neural networks Logic gates vision sensors

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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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Contrastive Embedding for Generalized Zero-Shot Learning

Contrastive Embedding for Generalized Zero-Shot Learning

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

作者： Han, Zongyan Fu, Zhenyong Chen, Shuo Yang, Jian Nanjing Univ Sci & Technol PCALab Nanjing Peoples R China RIKEN Ctr Adv Intelligence Project Wako Saitama Japan

ISBN: (纸本)9781665445092

Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL classification since it lacks discriminative information. To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework. The hybrid GZSL approach maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a contrastive embedding (CE) for our hybrid GZSL framework. The proposed contrastive embedding can leverage not only the class-wise supervision but also the instance-wise supervision, where the latter is usually neglected by existing GZSL researches. We evaluate our proposed hybrid GZSL framework with contrastive embedding, named CE-GZSL, on five benchmark datasets. The results show that our CE-GZSL method can outperform the state-of-the-arts by a significant margin on three datasets.

关键词： Visualization computer vision Codes Benchmark testing Hybrid power systems pattern recognition

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Xinhan Di Xiaokun Dai Xinkang Zhang Xinrong Chen Deepearthgo Academy for Engineering&Technology Fudan Universiry Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users’ virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results.

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Causal Attention for vision-Language Tasks

Causal Attention for Vision-Language Tasks

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

作者： Yang, Xu Zhang, Hanwang Qi, Guojun Cai, Jianfei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Futurewei Technol Santa Clara CA USA Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9781665445092

We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization. As the confounder is unobserved in general, we use the front-door adjustment to realize the causal intervention, which does not require any knowledge on the confounder. Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention. CATT abides by the QK-V convention and hence can replace any attention module such as top-down attention and self-attention in Transformers. CATT improves various popular attention-based vision-language models by considerable margins. In particular, we show that CATT has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT [57], which uses fewer data and less computational power, comparable to the heavier UNITER [14]. Code is published in https://***/yangxuntu/lxmertcatt.

关键词： computer vision Correlation Codes Computational modeling Training data Transformers Data models

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Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

Learning View-Disentangled Human Pose Representation by Cont...

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

作者： Zhao, Long Wang, Yuxiao Zhao, Jiaping Yuan, Liangzhe Sun, Jennifer J. Schroff, Florian Adam, Hartwig Peng, Xi Metaxas, Dimitris Liu, Ting Rutgers State Univ New Brunswick NJ 08901 USA Google Res Mountain View CA USA CALTECH Pasadena CA 91125 USA Univ Delaware Newark DE 19716 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. We further propose two regularization terms to ensure disentanglement and smoothness of the learned representations. The resulting pose representations can be used for cross-view action recognition. To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition. This task trains models with actions from only one single viewpoint while models are evaluated on poses captured from all possible viewpoints. We evaluate the learned representations on standard benchmarks for action recognition, and show that (i) CV-MIM performs competitively compared with the state-of-the-art models in the fully-supervised scenarios;(ii) CV-MIM outperforms other competing methods by a large margin in the single-shot cross-view setting;(iii) and the learned representations can significantly boost the performance when reducing the amount of supervised training data. Our code is made publicly available at https://***/google-research/google-research/tree/master/poem.

关键词： computer vision Codes Training data Benchmark testing Data models pattern recognition Task analysis

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Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting

Don't Look into the Dark: Latent Codes for Pluralistic Image...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haiwei Chen Yajie Zhao University of Southern California USC Institute for Creative Technologies

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a method for large-mask pluralistic image in-painting based on the generative framework of discrete latent codes. Our method learns latent priors, discretized as tokens, by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block, a bidirectional transformer that infers the missing labels by only looking at these tokens, and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.

关键词： Measurement Visualization computer vision Codes Benchmark testing Transformers pattern recognition

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Improving Transferability of Adversarial Patches on Face recognition with Generative Models

Improving Transferability of Adversarial Patches on Face Rec...

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

作者： Xiao, Zihao Gao, Xianfeng Fu, Chilin Dong, Yinpeng Gao, Wei Zhang, Xiaolu Zhou, Jun Zhu, Jun RealAI Beijing Peoples R China Ant Financial Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security sensitive applications. However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models. In this paper, we evaluate the robustness of face recognition models using adversarial patches based on transferability, where the attacker has limited accessibility to the target models. First, we extend the existing transfer-based attack techniques to generate transferable adversarial patches. However, we observe that the transferability is sensitive to initialization and degrades when the perturbation magnitude is large, indicating the overfitting to the substitute models. Second, we propose to regularize the adversarial patches on the low dimensional data manifold. The manifold is represented by generative models pre-trained on legitimate human face images. Using face-like features as adversarial perturbations through optimization on the manifold, we show that the gaps between the responses of substitute models and the target models dramatically decrease, exhibiting a better transferability. Extensive digital world experiments are conducted to demonstrate the superiority of the proposed method in the black-box setting. We apply the proposed method in the physical world as well.

关键词： Manifolds computer vision Face recognition Perturbation methods Authentication Robustness Security

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Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap

Are NeRFs ready for autonomous driving? Towards closing the ...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Carl Lindström Georg Hess Adam Lilja Maryam Fatemi Lars Hammarstrand Christoffer Petersson Lennart Svensson Zenseact Chalmers University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabilities. However, to trust the results achieved in simulation, one needs to ensure that AD systems perceive real and rendered data in the same way. Although the performance of rendering methods is increasing, many scenarios will remain inherently challenging to reconstruct faithfully. To this end, we propose a novel perspective for addressing the real-to-simulated data gap. Rather than solely focusing on improving rendering fidelity, we explore simple yet effective methods to enhance perception model robustness to NeRF artifacts without compromising performance on real data. Moreover, we conduct the first large-scale investigation into the real-to-simulated data gap in an AD setting using a state-of-the-art neural rendering technique. Specifically, we evaluate object detectors and an online mapping model on real and simulated data, and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data, even improving real-world performance in some cases. Last, we delve into the correlation between the real-to-simulated gap and image reconstruction metrics, identifying FID and LPIPS as strong indicators.

关键词： Measurement Object detection Neural radiance field Rendering (computer graphics) Data models Robustness pattern recognition

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