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

共 22908 条记录，以下是4351-4360 订阅

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Single Image Depth Prediction with Wavelet Decomposition

Single Image Depth Prediction with Wavelet Decomposition

引用

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

作者： Ramamonjisoa, Michael Firman, Michael Watson, Jamie Lepetit, Vincent Turmukhambetov, Daniyar Univ Gustave Eiffel CNRS Ecole Ponts IMAGINELIGM Champs Sur Marne Marne La Vallee France Niantic San Francisco CA USA

ISBN: (纸本)9781665445092

We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiplyadds in the decoder network.

关键词： Analytical models Neural networks Estimation computer architecture Wavelet analysis Decoding pattern recognition

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Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervi...

引用

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

作者： Taherkhani, Fariborz Dabouei, Ali Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665445092

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.

关键词： Measurement Training Clustering algorithms Semisupervised learning Data models pattern recognition Convolutional neural networks

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Can audio-visual integration strengthen robustness under multimodal attacks?

Can audio-visual integration strengthen robustness under mul...

引用

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

作者： Tian, Yapeng Xu, Chenliang Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

In this paper, we propose to make a systematic study on machines' multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks;audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks;even a weaklysupervised sound source visual localization model can be successfully fooled;our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance. The source code and pre-trained models are released in https://***/YapengTian/AV-Robustness-CVPR21.

关键词： Location awareness Visualization computer vision Systematics Codes Computational modeling Robustness

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引用

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

作者： Faraki, Masoud Yu, Xiang Tsai, Yi-Hsuan Suh, Yumin Chandraker, Manmohan NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face recognition [18], our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.

关键词： Training computer vision Filtering Face recognition Pipelines Benchmark testing Loss measurement

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Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

Objectron: A Large Scale Dataset of Object-Centric Videos in...

引用

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

作者： Ahmadyan, Adel Zhang, Liangkai Ablavatski, Artsiom Wei, Jianing Grundmann, Matthias Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14, 819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection and novel view synthesis tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at ***/google-research-datasets/Objectron.

关键词： Measurement Solid modeling Three-dimensional displays Annotations Shape Object detection pattern recognition

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Probabilistic Embeddings for Cross-Modal Retrieval

Probabilistic Embeddings for Cross-Modal Retrieval

引用

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

作者： Chun, Sanghyuk Oh, Seong Joon de Rezende, Rafael Sampaio Kalantidis, Yannis Larlus, Diane NAVER AI Lab Seongnam South Korea NAVER Labs Europe Meylan France

ISBN: (纸本)9781665445092

Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particularly challenging. Given an image (respectively a caption), there are multiple captions (respectively images) that equally make sense. In this paper, we argue that deterministic functions are not sufficiently powerful to capture such one-to-many correspondences. Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space. Since common benchmarks such as COCO suffer from non-exhaustive annotations for cross-modal matches, we propose to additionally evaluate retrieval on the CUB dataset, a smaller yet clean database where all possible image-caption pairs are annotated. We extensively ablate PCME and demonstrate that it not only improves the retrieval performance over its deterministic counterpart but also provides uncertainty estimates that render the embeddings more interpretable.

关键词： computer vision Uncertainty Codes Databases Annotations Tools Benchmark testing

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StereoPIFu: Depth Aware Clothed Human Digitization via Stereo vision

StereoPIFu: Depth Aware Clothed Human Digitization via Stere...

引用

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

作者： Hong, Yang Zhang, Juyong Jiang, Boyi Guo, Yudong Liu, Ligang Bao, Hujun Univ Sci & Technol China Hefei Anhui Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction. Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details. Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality. Consequently, our StereoPIFu can naturally infer the human body's spatial location in camera space and maintain the correct relative position of different parts of the human body, which enables our method to capture human performance. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction, which is demonstrated by extensive experimental results.

关键词： Geometry Surface reconstruction computer vision Three-dimensional displays Shape Robustness Stereo vision

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TattTRN: Template Reconstruction Network for Tattoo Retrieval

TattTRN: Template Reconstruction Network for Tattoo Retrieva...

引用

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

作者： Lazaro Janier Gonzalez-Soler Maciej Salwowski Christian Rathgeb Daniel Fischer da/sec - Biometrics and Security Research Group Darmstadt Germany Faculty of Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of offenders and victims, as they contain discriminative information, and are a useful indicator to locate members of a criminal gang or organisation. Due to various privacy issues in the acquisition of images containing tattoos, only a limited number of databases exists. This lack of databases has delayed the development of new methods to effectively retrieve a potential suspect’s tattoo images from a candidate gallery. To mitigate this issue, in our work, we use an unsupervised generative approach to create a balanced database consisting of 28,550 semi-synthetic images with tattooed subjects from 571 tattoo categories. Further, we introduce a novel Tattoo Template Reconstruction Network (TattTRN), which learns to map the input tattoo sample to its respective tattoo template to enhance the distinguishing attributes of the final feature embedding. Experimental results with real data, i.e. WebTattoo and BIVTatt databases, demonstrate the soundness of the presented approach: an accuracy of up to 99% is achieved for checking at most the first 20 entries of the candidate list. 1

关键词： Image segmentation Data privacy computer vision Databases Law enforcement conferences Semantics

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ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer

ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fou...

引用

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

作者： Wei Dong Han Zhou Yuqiong Tian Jingke Sun Xiaohong Liu Guangtao Zhai Jun Chen McMaster University Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://***/movingforward100/Shadow_R.

关键词： Representation learning Image quality Image segmentation computer vision Image color analysis conferences Lighting

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Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Weakly Supervised Set-Consistency Learning Improves Morpholo...

引用

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

作者： Heming Yao Phil Hanslovsky Jan-Christian Huetter Burkhard Hoeckendorf David Richmond Biology Research | AI Development (BRAID) gCS Genentech

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-content images remains an active area of research and is currently undergoing rapid innovation through the application of self-supervised learning and vision transformers. In this study, we propose a set-level consistency learning algorithm, Set-DINO, that combines self-supervised learning with weak supervision to improve learned representations of perturbation effects in single-cell images. Our method leverages the replicate structure of OPS experiments (i.e., cells undergoing the same genetic perturbation, both within and across batches) as a form of weak supervision. We conduct extensive experiments on a large-scale OPS dataset with more than 5000 genetic perturbations, and demonstrate that Set-DINO helps mitigate the impact of confounders and encodes more biologically meaningful information. In particular, Set-DINO recalls known biological relationships with higher accuracy compared to commonly used methods for morphological profiling, suggesting that it can generate more reliable insights from drug target discovery campaigns leveraging OPS.

关键词： computer vision Technological innovation Biomedical optical imaging Perturbation methods Self-supervised learning Optical imaging Genetics

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