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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training

The Enemy of My Enemy is My Friend: Exploring Inverse Advers...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Junhao Dong Seyed-Mohsen Moosavi-Dezfooli Jianhuang Lai Xiaohua Xie School of Computer Science and Engineering Sun Yat-Sen University China Imperial College London UK Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial training and its variants have been shown to be the most effective approaches to defend against adversarial examples. A particular class of these methods regularize the difference between output probabilities for an adversarial and its corresponding natural example. However, it may have a negative impact if a natural example is misclassified. To circumvent this issue, we propose a novel adversarial training scheme that encourages the model to produce similar output probabilities for an adversarial example and its “inverse adversarial” counterpart. Particularly, the counterpart is generated by maximizing the likelihood in the neighborhood of the natural example. Extensive experiments on various vision datasets and architectures demonstrate that our training method achieves state-of-the-art robustness as well as natural accuracy among robust models. Furthermore, using a universal version of inverse adversarial examples, we improve the performance of single-step adversarial training techniques at a low computational cost.

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High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jin Han Yuta Asano Boxin Shi Yinqiang Zheng Imari Sato Graduate School of Information Science and Technology The University of Tokyo National Institute of Informatics National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

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Filtering, Distillation, and Hard Negatives for vision-Language Pre-Training

Filtering, Distillation, and Hard Negatives for Vision-Langu...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Filip Radenovic Abhimanyu Dubey Abhishek Kadian Todor Mihaylov Simon Vandenhende Yash Patel Yi Wen Vignesh Ramanathan Dhruv Mahajan Meta AI CTU in Prague

vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve the following three aspects of the contrastive pre-training pipeline: dataset noise, model initialization and the training objective. First, we propose a straightforward filtering strategy titled Complexity, Action, and Text-spotting (CAT) that significantly reduces dataset size, while achieving improved performance across zero-shot vision-language tasks. Next, we propose an approach titled Concept Distillation to leverage strong unimodal representations for contrastive training that does not increase training complexity while outperforming prior work. Finally, we modify the traditional contrastive alignment objective, and propose an importance-sampling approach to up-sample the importance of hard-negatives without adding additional complexity. On an extensive zero-shot benchmark of 29 tasks, our Distilled and Hard-negative Training (DiHT) approach improves on 20 tasks compared to the baseline. Furthermore, for few-shot linear probing, we propose a novel approach that bridges the gap between zero-shot and few-shot performance, substantially improving over prior work. Models are available at ***/facebookresearch/diht.

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DKM: Dense Kernelized Feature Matching for Geometry Estimation

DKM: Dense Kernelized Feature Matching for Geometry Estimati...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Johan Edstedt Ioannis Athanasiadis Mårten Wadenbäck Michael Felsberg Computer Vision Laboratory Linköping University

Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, Dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC@5° compared to the best previous sparse method and dense method respectively. Our code is provided at the following repository: https://***/Parskatt/DKM.

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Deep Random Projector: Accelerated Deep Image Prior

Deep Random Projector: Accelerated Deep Image Prior

引用

conference on computer vision and pattern recognition (CVPR)

作者： Taihui Li Hengkang Wang Zhong Zhuang Ju Sun Computer Science and Engineering University of Minnesota Minneapolis USA Electrical and Computer Engineering University of Minnesota Minneapolis USA

Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior-encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://***/sun-umn/Deep-Random-Projector.

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Bojana Gajić Ariel Amato Ramon Baldrich Joost van de Weijer Carlo Gatta Vintra Inc. Barcelona Spain Computer Vision Center Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

ActMAD: Activation Matching to Align Distributions for Test-...

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conference on computer vision and pattern recognition (CVPR)

作者： M. Jehanzeb Mirza Pol Jané Soneira Wei Lin Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture-and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance.

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Local Implicit Ray Function for Generalizable Radiance Field Representation

Local Implicit Ray Function for Generalizable Radiance Field...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xin Huang Qi Zhang Ying Feng Xiaoyu Li Xuan Wang Qing Wang School of Computer Science Northwestern Polytechnical University Xi'an China Tencent AI Lab

We propose LIRF (Local Implicit Ray Function), a generalizable neural rendering approach for novel view rendering. Current generalizable neural radiance fields (NeRF) methods sample a scene with a single ray per pixel and may therefore render blurred or aliased views when the input views and rendered views capture scene content with different resolutions. To solve this problem, we propose LIRF to aggregate the information from conical frustums to construct a ray. Given 3D positions within conical frustums, LIRF takes 3D coordinates and the features of conical frustums as inputs and predicts a local volumetric radiance field. Since the coordinates are continuous, LIRF renders high-quality novel views at a continuously-valued scale via volume rendering. Besides, we predict the visible weights for each input view via transformer-based feature matching to improve the performance in occluded areas. Experimental results on real-world scenes validate that our method outperforms state-of-the-art methods on novel view rendering of unseen scenes at arbitrary scales.

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Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections

Iterative Next Boundary Detection for Instance Segmentation ...

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conference on computer vision and pattern recognition (CVPR)

作者： Alexander Gillert Giulia Resente Alba Anadon-Rosell Martin Wilmking Uwe Freiherr Von Lukas Fraunhofer Institute for Computer Graphics Research (IGD) Rostock Institute of Botany and Landscape Ecology Ernst Moritz Arndt University Greifswald Centre for Research on Ecology and Forestry Applications (CREAF) Barcelona Institute for Visual & Analytic Computing University of Rostock

We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate performance of existing methods. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://***/alexander-g/INBD.

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Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks

Efficient Loss Function by Minimizing the Detrimental Effect...

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conference on computer vision and pattern recognition (CVPR)

作者： Yunrui Yu Cheng-Zhong Xu Department of Computer Science State Key Lab of IOTSC University of Macau Macau SAR China

Attackers can deceive neural networks by adding human imperceptive perturbations to their input data; this reveals the vulnerability and weak robustness of current deep-learning networks. Many attack techniques have been proposed to evaluate the model's robustness. Gradient-based attacks suffer from severely overestimating the robustness. This paper identifies that the relative error in calculated gradients caused by floating-point errors, including floating-point underflow and rounding errors, is a fundamental reason why gradient-based attacks fail to accurately assess the model's robustness. Although it is hard to eliminate the relative error in the gradients, we can control its effect on the gradient-based attacks. Correspondingly, we propose an efficient loss function by minimizing the detrimental impact of the floating-point errors on the attacks. Experimental results show that it is more efficient and reliable than other loss functions when examined across a wide range of defence mechanisms.

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