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Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers 32

Modulating Image Restoration with Continual Levels via Adapt...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： He, Jingwen Dong, Chao Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of little additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.

关键词： Deep Learning Low-level vision

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Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

Leverage Your Local and Global Representations: A New Self-S...

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

作者： Zhang, Tong Qiu, Congpei Ke, Wei Suesstrunk, Sabine Salzmann, Mathieu Ecole Polytech Fed Lausanne Sch Comp & Commun Sci Lausanne Switzerland Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same image regardless of cropping size and content. In essence, this strategy ignores the fact that two crops may truly contain different image information, e.g., background and small objects, and thus tends to restrain the diversity of the learned representations. In this work, we address this issue by introducing a new self-supervised learning strategy, LoGo, that explicitly reasons about Local and Global crops. To achieve view invariance, LoGo encourages similarity between global crops from the same image, as well as between a global and a local crop. However, to correctly encode the fact that the content of smaller crops may differ entirely, LoGo promotes two local crops to have dissimilar representations, while being close to global crops. Our LoGo strategy can easily be applied to existing SSL methods. Our extensive experiments on a variety of datasets and using different self-supervised learning frameworks validate its superiority over existing approaches. Noticeably, we achieve better results than supervised models on transfer learning when using only 1/10 of the data.

关键词： Representation learning computer vision Codes Computational modeling Crops Self-supervised learning Feature extraction

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Super-Resolution Appearance Transfer for 4D Human Performances

Super-Resolution Appearance Transfer for 4D Human Performanc...

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

作者： Pesavento, Marco Volino, Marco Hilton, Adrian Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and capture volume. Typically the requirement to frame cameras to capture the volume of a dynamic performance (> 50m(3)) results in the person occupying only a small proportion < 10% of the field of view. Even with ultra high-definition 4k video acquisition this results in sampling the person at less-than standard definition 0.5k video resolution resulting in low-quality rendering. In this paper we propose a solution to this problem through super-resolution appearance transfer from a static high-resolution appearance capture rig using digital stills cameras (> 8k) to capture the person in a small volume (< 8m(3)). A pipeline is proposed for super-resolution appearance transfer from high-resolution static capture to dynamic video performance capture to produce super-resolution dynamic textures. This addresses two key problems: colour mapping between different camera systems;and dynamic texture map super-resolution using a learnt model. Comparative evaluation demonstrates a significant qualitative and quantitative improvement in rendering the 4D performance capture with super-resolution dynamic texture appearance. The proposed approach reproduces the high-resolution detail of the static capture whilst maintaining the appearance dynamics of the captured video.

关键词： Surface reconstruction Image color analysis Superresolution Pipelines Cameras Rendering (computer graphics) Surface texture

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Roses are Red, Violets are Blue... But Should VQA expect Them To?

Roses are Red, Violets are Blue... But Should VQA expect The...

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

作者： Kervadec, Corentin Antipov, Grigory Baccouche, Moez Wolf, Christian Cesson Seyigne Orange France INSA Lyon LIRIS UMR CNRS 5205 Lyon France

ISBN: (纸本)9781665445092

Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to "reason", leading them to perform "educated guesses" instead. In this paper, we claim that the standard evaluation metric, which consists in measuring the overall in-domain accuracy, is misleading. Since questions and concepts are unbalanced, this tends to favor models which exploit subtle training set statistics. Alternatively, naively introducing artificial distribution shifts between train and test splits is also not completely satisfying. First, the shifts do not reflect real-world tendencies, resulting in unsuitable models;second, since the shifts are handcrafted, trained models are specifically designed for this particular setting, and do not generalize to other configurations. We propose the GQAOOD benchmark designed to overcome these concerns: we measure and compare accuracy over both rare and frequent question-answer pairs, and argue that the former is better suited to the evaluation of reasoning abilities, which we experimentally validate with models trained to more or less exploit biases. In a large-scale study involving 7 VQA models and 3 bias reduction techniques, we also experimentally demonstrate that these models fail to address questions involving infrequent concepts and provide recommendations for future directions of research.

关键词： Training Measurement Visualization computer vision Computational modeling Benchmark testing Knowledge discovery

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Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation

Exploring Category-Agnostic Clusters for Open-Set Domain Ada...

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

作者： Pan, Yingwei Yao, Ting Li, Yehao Ngo, Chong-Wah Mei, Tao JD AI Res Beijing Peoples R China City Univ Hong Kong Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781728171685

Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.

关键词： computer vision

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Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation

Reusing the Task-specific Classifier as a Discriminator: Dis...

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

作者： Chen, Lin Chen, Huaian Wei, Zhixiang Jin, Xin Tan, Xiao Jin, Yi Chen, Enhong Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Adversarial learning has achieved remarkable performances for unsupervised domain adaptation (UDA). Existing adversarial UDA methods typically adopt an additional discriminator to play the min-max game with a feature extractor. However, most of these methods failed to effectively leverage the predicted discriminative information, and thus cause mode collapse for generator. In this work, we address this problem from a different perspective and design a simple yet effective adversarial paradigm in the form of a discriminator-free adversarial learning network (DALN), wherein the category classifier is reused as a discriminator, which achieves explicit domain alignment and category distinguishment through a unified objective, enabling the DALN to leverage the predicted discriminative information for sufficient feature alignment. Basically, we introduce a Nuclear-norm Wasserstein discrepancy (NWD) that has definite guidance meaning for performing discrimination. Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy. Without bells and whistles, DALN compares favorably against the existing state-of-the-art (SOTA) methods on a variety of public datasets. Moreover, as a plug-and-play technique, NWD can be directly used as a generic regularizer to benefit existing UDA algorithms. Code is available at https://***/xiaoachen98/DALN.

关键词： computer vision Codes Games Predictive models Feature extraction Adversarial machine learning Generators

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All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

All Labels Are Not Created Equal: Enhancing Semi-supervision...

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

作者： Nassar, Islam Herath, Samitha Abbasnejad, Ehsan Buntine, Wray Haffari, Gholamreza Monash Univ Dept Data Sci & AI Fac IT Clayton Vic Australia Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9781665445092

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance.

关键词： Training Visualization Semantics Semisupervised learning Predictive models pattern recognition Labeling

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Improving Adversarial Transferability via Neuron Attribution-based Attacks

Improving Adversarial Transferability via Neuron Attribution...

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

作者： Zhang, Jianping Wu, Weibin Huang, Jen-tse Huang, Yizhan Wang, Wenxuan Su, Yuxin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To efficiently tackle the black-box setting where the target model's particulars are unknown, feature-level transfer-based attacks propose to contaminate the intermediate feature outputs of local models, and then directly employ the crafted adversarial samples to attack the target model. Due to the transferability of features, feature-level attacks have shown promise in synthesizing more transferable adversarial samples. However, existing feature-level attacks generally employ inaccurate neuron importance estimations, which deteriorates their transferability. To overcome such pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA), which conducts feature-level attacks with more accurate neuron importance estimations. Specifically, we first completely attribute a model's output to each neuron in a middle layer. We then derive an approximation scheme of neuron attribution to tremendously reduce the computation overhead. Finally, we weight neurons based on their attribution results and launch feature-level attacks. Extensive experiments confirm the superiority of our approach to the state-of-the-art benchmarks. Our code is available at: https://***/jpzhang1810/NAA.

关键词： Deep learning computer vision Codes Computational modeling Neurons Neural networks Estimation

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Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View

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

作者： Zhou, Tianfei Wang, Wenguan Konukoglu, Ender Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Technol Sydney AAII ReLER Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Prevalent semantic segmentation solutions, despite their different network designs (FCN based or attention based) and mask decoding strategies (parametric softmax based or pixel-query based), can be placed in one category, by considering the softmax weights or query vectors as learnable class prototypes. In light of this prototype view, this study uncovers several limitations of such parametric segmentation regime, and proposes a nonparametric alternative based on non-learnable prototypes. Instead of prior methods learning a single weight/query vector for each class in a fully parametric manner, our model represents each class as a set of non-learnable prototypes, relying solely on the mean features of several training pixels within that class. The dense prediction is thus achieved by nonparametric nearest prototype retrieving. This allows our model to directly shape the pixel embedding space, by optimizing the arrangement between embedded pixels and anchored prototypes. It is able to handle arbitrary number of classes with a constant amount of learnable parameters. We empirically show that, with FCN based and attention based segmentation models (i.e., HRNet, Swin, SegFormer) and backbones (i.e., ResNet, HRNet, Swin, MiT), our nonparametric framework yields compelling results over several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in the large-vocabulary situation. We expect this work will provoke a rethink of the current de facto semantic segmentation model design.

关键词： Training computer vision Image analysis Shape Computational modeling Semantics Prototypes

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The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth

The Temporal Opportunist: Self-Supervised Multi-Frame Monocu...

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

作者： Watson, Jamie Mac Aodha, Oisin Prisacariu, Victor Brostow, Gabriel Firman, Michael Niantic San Francisco CA 94111 USA Univ Edinburgh Edinburgh Midlothian Scotland Univ Oxford Oxford England UCL London England

ISBN: (纸本)9781665445092

Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.

关键词： Training computer vision Costs Adaptive systems Computational modeling Estimation Cameras

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