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OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervi...

引用

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

作者： Gidaris, Spyros Bursuc, Andrei Puy, Gilles Komodakis, Nikos Cord, Matthieu Perez, Patrick Valeo Ai Paris France Univ Crete Iraklion Greece Sorbonne Univ Paris France

ISBN: (纸本)9781665445092

Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although effective visual representations should indeed exhibit such invariances, there are other important characteristics, such as encoding contextual reasoning skills, for which alternative reconstruction-based approaches might be better suited. With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image. Our strategy performs an online training of both the teacher network (whose role is to generate the BoW targets) and the student network (whose role is to learn representations), along with an online update of the visual-words vocabulary (used for the BoW targets). This idea effectively enables fully online BoW-guided unsupervised learning. Extensive experiments demonstrate the interest of our BoW-based strategy, which surpasses previous state-of-the-art methods (including contrastive-based ones) in several applications. For instance, in downstream tasks such Pascal object detection, Pascal classification and Places205 classification, our method improves over all prior unsupervised approaches, thus establishing new state-of-the-art results that are also significantly better even than those of supervised pre-training. We provide the implementation code https://***/valeoai/obow.

关键词： Training Vocabulary Visualization Protocols Perturbation methods Object detection pattern recognition

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Face Forgery Detection by 3D Decomposition

Face Forgery Detection by 3D Decomposition

引用

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

作者： Zhu, Xiangyu Wang, Hao Fei, Hongyan Lei, Zhen Li, Stan Z. Chinese Acad Sci Inst Automat CBSR Beijing Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot Hong Kong Inst Sci & Innovat Beijing Peoples R China Westlake Univ Sch Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment, and decompose it in a computer graphics view. Specifically, by disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. Based on this observation, we propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns. Besides, we highlight the manipulated region with a supervised attention mechanism and introduce a two-stream structure to exploit both face image and facial detail together as a multi-modality task. Extensive experiments indicate the effectiveness of the extra features extracted from the facial detail, and our method achieves the state-of-the-art performance.

关键词： Geometry computer vision Three-dimensional displays Shape Face recognition Lighting Production

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DAT: Training Deep Networks Robust to Label-Noise by Matching the Feature Distributions

DAT: Training Deep Networks Robust to Label-Noise by Matchin...

引用

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

作者： Qu, Yuntao Mo, Shasha Niu, Jianwei Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China Zhengzhou Univ Res Inst Ind Technol Zhengzhou Henan Peoples R China

ISBN: (纸本)9781665445092

In real application scenarios, the performance of deep networks may be degraded when the dataset contains noisy labels. Existing methods for learning with noisy labels are limited by two aspects. Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels. Secondly, others based on the memorization effect outperform in synthetic noise but get weak promotion in real-world noisy datasets. To solve these problems, this paper proposes a novel label-noise robust method named Discrepant Adversarial Training (DAT). The DAT method has ability of enforcing prominent feature extraction by matching feature distribution between clean and noisy data. Therefore, under the noise-free feature representation, the deep network can simply output the correct result. To better capture the divergence between the noisy and clean distribution, a new metric is designed to change the distribution divergence into computable. By minimizing the proposed metric with a min-max training of discrepancy on classifiers and generators, DAT can match noisy data to clean data in the feature space. To the best of our knowledge, DAT is the first to address the noisy label problem from the perspective of the feature distribution. Experiments on synthetic and real-world noisy datasets demonstrate that DAT can consistently outperform other state-of-the-art methods. Codes are available at https://***/Tygnn0323/DAT.

关键词： Training computer vision Codes Feature extraction Extraterrestrial measurements Generators pattern recognition

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Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

Physics-based Iterative Projection Complex Neural Network fo...

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

作者： Zhang, Feilong Liu, Xianming Guo, Cheng Lin, Shiyi Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. However, their interpretability and generalization still remain a major challenge. In this paper, we propose to combine the advantages of both model-based alternative projection method and deep neural network for phase retrieval, so as to achieve network interpretability and inference effectiveness simultaneously. Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow. The physical model of the imaging process is then naturally embedded into the neural network structure. Moreover, a complex-valued U-Net is proposed for defining image priori for forward and backward projection in dual planes. Finally, we designate physics-based formulation as an untrained deep neural network, whose weights are enforced to fit to the given intensity measurements. In summary, our scheme for phase retrieval is effective, interpretable, physics-based and unsupervised. Experimental results demonstrate that our method achieves superior performance compared with the state-of-the-arts in a practical phase retrieval application-lensless microscopy imaging.

关键词： Deep learning Weight measurement Phase measurement Microscopy Neural networks Loss measurement pattern recognition

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vision DiffMask: Faithful Interpretation of vision Transformers with Differentiable Patch Masking

Vision DiffMask: Faithful Interpretation of Vision Transform...

引用

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

作者： Angelos Nalmpantis Apostolos Panagiotopoulos John Gkountouras Konstantinos Papakostas Wilker Aziz University of Amsterdam

The lack of interpretability of the vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called vision DiffMask, which uses the activations of the model’s hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation here.

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Bidirectional Projection Network for Cross Dimension Scene Understanding

Bidirectional Projection Network for Cross Dimension Scene U...

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

作者： Hu, Wenbo Zhao, Hengshuang Jiang, Li Jia, Jiaya Wong, Tien-Tsin Chinese Univ Hong Kong Hong Kong Peoples R China Univ Oxford Oxford England Chinese Acad Sci SIAT Shenzhen Key Lab Virtual Real & Human Interact Te Beijing Peoples R China

ISBN: (纸本)9781665445092

2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation.

关键词： Geometry Visualization computer vision Three-dimensional displays Semantics Benchmark testing Image representation

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Context Modeling in 3D Human Pose Estimation: A Unified Perspective

Context Modeling in 3D Human Pose Estimation: A Unified Pers...

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

作者： Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Ci, Hai Wang, Yizhou Peking Univ Ctr Frontiers Comp Studies Dept Comp Sci Beijing Peoples R China Peking Univ Ctr Data Sci Adv Inst Info Tech Beijing Peoples R China Beijing Film Acad Adv Innovat Ctr Future Visual Entertainment AICFV Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance.

关键词： Training computer vision Three-dimensional displays Pose estimation Benchmark testing Graph neural networks pattern recognition

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Distilling Object Detectors via Decoupled Features

Distilling Object Detectors via Decoupled Features

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

作者： Guo, Jianyuan Han, Kai Wang, Yunhe Wu, Han Chen, Xinghao Xu, Chunjing Xu, Chang Huawei Technol Noahs Ark Lab Beijing Peoples R China Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781665445092

Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance. Different from image classification, object detectors are much more sophisticated with multiple loss functions in which features that semantic information rely on are tangled. In this paper, we point out that the information of features derived from regions excluding objects are also essential for distilling the student detector, which is usually ignored in existing approaches. In addition, we elucidate that features from different regions should be assigned with different importance during distillation. To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector. Specifically, two levels of decoupled features will be processed for embedding useful information into the student, i.e., decoupled features from neck and decoupled proposals from classification head. Extensive experiments on various detectors with different backbones show that the proposed DeFeat is able to surpass the state-of-the-art distillation methods for object detection. For example, DeFeat improves ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50 based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Code will be released(1,2).

关键词： Knowledge engineering Semantics Detectors Object detection Feature extraction pattern recognition Neck

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Simulating Unknown Target Models for Query-Efficient Black-box Attacks

Simulating Unknown Target Models for Query-Efficient Black-b...

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

作者： Ma, Chen Chen, Li Yong, Jun-Hai Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. In the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the query complexity remains high, and such attacks can be defended easily. This study aims to train a generalized substitute model called "Simulator", which can mimic the functionality of any unknown target model. To this end, we build the training data with the form of multiple tasks by collecting query sequences generated during the attacks of various existing networks. The learning process uses a mean square error-based knowledge-distillation loss in the meta-learning to minimize the difference between the Simulator and the sampled networks. The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization. When attacking a target model that is unseen in training, the trained Simulator can accurately simulate its functionality using its limited feedback. As a result, a large fraction of queries can be transferred to the Simulator, thereby reducing query complexity. Results of the comprehensive experiments conducted using the CIFAR-10, CIFAR-100, and TinyImageNet datasets demonstrate that the proposed approach reduces query complexity by several orders of magnitude compared to the baseline method. The implementation source code is released online(1).

关键词： Training Deep learning computer vision Codes Computational modeling Training data Complexity theory

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Adaptive Image Transformer for One-Shot Object Detection

Adaptive Image Transformer for One-Shot Object Detection

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

作者： Chen, Ding-Jie Hsieh, He-Yen Liu, Tyng-Luh Acad Sinica Inst Informat Sci Taipei Taiwan Taiwan AI Labs Taipei Taiwan

ISBN: (纸本)9781665445092

One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available in the training data. Our main idea leverages the concept of language translation to boost metric-learning-based detection methods. Specifically, we emulate the language translation process to adaptively translate the feature of each object proposal to better correlate the given query feature for discriminating the class-similarity among the proposal-query pairs. To this end, we propose the Adaptive Image Transformer (AIT) module that deploys an attention-based encoder-decoder architecture to simultaneously explore intra-coder and inter-coder (i.e., each proposal-query pair) attention. The adaptive nature of our design turns out to be flexible and effective in addressing the one-shot learning scenario. With the informative attention cues, the proposed model excels in predicting the class-similarity between the target image proposals and the query image patch. Though conceptually simple, our model significantly outperforms a state-of-the-art technique, improving the unseenclass object classification from 63.8 mAP and 22.0 AP50 to 72.2 mAP and 24.3 AP50 on the PASCAL-VOC and MS-COCO benchmark datasets, respectively.

关键词： Adaptation models Training data Object detection Predictive models Feature extraction Transformers pattern recognition

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