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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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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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Open-set Fine-grained Retrieval via Prompting vision-Language Evaluator

Open-set Fine-grained Retrieval via Prompting Vision-Languag...

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

作者： Wang, Shijie Chang, Jianlong Li, Haojie Wang, Zhihui Ouyang, Wanli Tian, Qi Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Huawei Cloud & AI Shenzhen Peoples R China Shandong Univ Sci & Technol Coll Comp & Engn Qingdao Peoples R China Univ Sydney Sense Time Comp Vis Res Grp Sydney NSW Australia

ISBN: (纸本)9798350301298

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词： vision applications and systems

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Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable plann...

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

作者： Ishida, Shu Henriques, Joao F. Univ Oxford Visual Geometry Grp Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We train embodied neural networks to plan and navigate unseen complex 3D environments, emphasising real-world deployment. Rather than requiring prior knowledge of the agent or environment, the planner learns to model the state transitions and rewards. lb avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations. Although they work well in small simulations, we address two major limitations that hinder their deployment. First, we observed that current differentiable planners struggle to plan long-term in environments with a high branching complexity. While they should ideally learn to assign low rewards to obstacles to avoid collisions, these penalties are not strong enough to guarantee collision-free operation. We thus impose a structural constraint on the value iteration, which explicitly learns to model impossible actions and noisy motion. Secondly, we extend the model to plan exploration with a limited perspective camera under translation and fine rotations, which is crucial for real robot deployment. Our proposals significantly improve semantic navigation and exploration on several 2D and 3D environments, succeeding in settings that are otherwise challenging for differentiable planners. As far as we know, we are the first to successfully apply them to the difficult Active vision Dataset, consisting of real images captured from a robot.(1)

关键词： Representation learning Three-dimensional displays Navigation Robot vision systems Semantics Neural networks Reinforcement learning

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A regularized spectral algorithm for Hidden Markov Models with applications in computer vision

A regularized spectral algorithm for Hidden Markov Models wi...

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2012 ieee conference on computer vision and pattern recognition, cvpr 2012

作者： Minh, Ha Quang Cristani, Marco Perina, Alessandro Murino, Vittorio Genoa 16163 Italy Microsoft Research WA United States

ISBN: (纸本)9781467312264

Hidden Markov Models (HMMs) are among the most important and widely used techniques to deal with sequential or temporal data. Their application in computer vision ranges from action/gesture recognition to videosurveillance through shape analysis. Although HMMs are often embedded in complex frameworks, this paper focuses on theoretical aspects of HMM learning. We propose a regularized algorithm for learning HMMs in the spectral framework, whose computations have no local minima. Compared with recently proposed spectral algorithms for HMMs, our method is guaranteed to produce probability values which are always physically meaningful and which, on synthetic mathematical models, give very good approximations to true probability values. Furthermore, we place no restriction on the number of symbols and the number of states. On various pattern recognition data sets, our algorithm consistently outperforms classical HMMs, both in accuracy and computational speed. This and the fact that HMMs are used in vision as building blocks for more powerful classification approaches, such as generative embedding approaches or more complex generative models, strongly support spectral HMMs (SHMMs) as a new basic tool for pattern recognition. © 2012 ieee.

关键词： Hidden Markov models

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Clusformer: A Transformer based Clustering Approach to Unsupervised Large-scale Face and Visual Landmark recognition

Clusformer: A Transformer based Clustering Approach to Unsup...

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

作者： Xuan-Bac Nguyen Duc Toan Bui Chi Nhan Duong Bui, Tien D. Luu, Khoa VinAI Res Hanoi Vietnam Concordia Univ Montreal PQ Canada Univ Arkansas Fayetteville AR 72701 USA

ISBN: (纸本)9781665445092

The research in automatic unsupervised visual clustering has received considerable attention over the last couple years. It aims at explaining distributions of unlabeled visual images by clustering them via a parameterized model of appearance. Graph Convolutional Neural Networks (GCN) have recently been one of the most popular clustering methods. However, it has reached some limitations. Firstly, it is quite sensitive to hard or noisy samples. Secondly, it is hard to investigate with various deep network models due to its computational training time. Finally, it is hard to design an end-to-end training model between the deep feature extraction and GCN clustering modeling. This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism. The proposed method is able to robustly deal with noisy or hard samples. It is also flexible and effective to collaborate with different deep network models with various model sizes in an end-to-end framework. The proposed method is evaluated on two popular large-scale visual databases, i.e. Google Landmark and MS-Celeb1M face database, and outperforms prior unsupervised clustering methods. Code will be available at https: //***/VinAIResearch/Clusformer

关键词： Training Visualization Face recognition Computational modeling Clustering methods Transformers Encoding

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recognition BY USING AN ACTIVE SPACE-VARIANT SENSOR

RECOGNITION BY USING AN ACTIVE SPACE-VARIANT SENSOR

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1994 ieee computer-Society conference on computer vision and pattern recognition

作者： TISTARELLI, M UNIV GENOA DISTINTEGRATED ADV ROBOT LABI-16145 GENOAITALY

ISBN: (纸本)0818658258

The problem of object recognition is addressed. In the literature this task has been generally considered in a 'passive' perspective, where everything is static and there is no definite relation between the object and its environment. We propose an 'active' approach for object recognition, based on the capability of the observer to move and give a better description of the object under consideration and also to take advantage of the relations between the objects and the environment. This can be accomplished at the task level and at the sensor level. The face recognition problem, based on the face-space approach, is considered to demonstrate the advantage of adopting an active retina to sample the face, build a database and perform the recognition task. By using an active space-variant retina the size of the database is considerably reduced and consequently also the processing time for recognition. A comparative experiment using the active and static approach is presented.

关键词： pattern recognition

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M²SGD: Learning to Learn ImportantWeights

M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights

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

作者： Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland

ISBN: (纸本)9781728193601

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-crafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s. We release our codes at https://***/Nic5472K/CLvision2020_cvpr_M2SGD.

关键词： computer vision

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Few-Shot Object recognition from Machine-Labeled Web Images 30

Few-Shot Object Recognition from Machine-Labeled Web Images

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Zhongwen Zhu, Linchao Yang, Yi Univ Technol Sydney CAI Sydney NSW Australia

ISBN: (纸本)9781538604571

With the tremendous advances made by Convolutional Neural Networks (ConvNets) on object recognition, we can now easily obtain adequately reliable machine-labeled annotations easily from predictions by off-the-shelf ConvNets. In this work, we present an "abstraction memory" based framework for few-shot learning, building upon machinelabeled image annotations. Our method takes large-scale machine-annotated dataset (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots in the form of keyvalue, in which image feature is regarded as the key and the label embedding serves as the value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank and writes the useful information obtained from related external data into another memory bank, i.e. abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory correlates with the query example. The abstraction memory concentrates information from the external memory bank to make the few-shot recognition effective. In the experiments, we first confirm that our model can learn to conduct few-shot object recognition on clean humanlabeled data from the ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources for performing object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great results, with only a 1% difference in accuracy between the machine-labeled annotations and the human-labeled annotations.

关键词： Memory Bank external memory object recognition ANNOTATIONS Banks image feature Abstraction

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Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking 31

Learning Attentions: Residual Attentional Siamese Network fo...

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

作者： Wang, Qiang Teng, Zhu Xing, Junliang Gao, Jin Hu, Weiming Maybank, Stephen University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Information Systems Birkbeck College University of London UK

ISBN: (纸本)9781538664209

Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online. In particular, by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning. The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results on two latest benchmarks, OTB-2015 and VOT2017, show that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second.

关键词： Target tracking Correlation Feature extraction Training Adaptation models Visualization Object tracking

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