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RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features

RefineMask: Towards High-Quality Instance Segmentation with ...

引用

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

作者： Zhang, Gang Lu, Xin Tan, Jingru Li, Jianmin Zhang, Zhaoxiang Li, Quanquan Hu, Xiaolin Tsinghua Univ State Key Lab Intelligent Technol & Syst Inst AI BNRistDept Comp Sci & Technol Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Tongji Univ Shanghai Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China UCAS Beijing Peoples R China

ISBN: (纸本)9781665445092

The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https.//***/zhanggang001/RefineMask.

关键词： computer vision Codes Benchmark testing Feature extraction pattern recognition Computational efficiency

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Dissecting the High-Frequency Bias in Convolutional Neural Networks

Dissecting the High-Frequency Bias in Convolutional Neural N...

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

作者： Abello, Antonio A. Hirata Jr, Roberto Wang, Zhangyang Univ Sao Paulo Butanta Sao Paulo SP Brazil Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665448994

For convolutional neural networks (CNNs), a common hypothesis that explains both their generalization capability and their characteristic brittleness is that these models are implicitly regularized to rely on imperceptible high-frequency patterns, more than humans would do. This hypothesis has seen some empirical validation, but most works do not rigorously divide the image frequency spectrum. We present a model to divide the spectrum in disjointed discs based on the distribution of energy and apply simple feature importance procedures to test whether high-frequencies are more important than lower ones. We find evidence that mid or high-level frequencies are disproportionately important for CNNs. The evidence is robust across different datasets and networks. Moreover, we find the diverse effects of the network's attributes, such as architecture and depth, on frequency bias and robustness in general.

关键词： computer vision conferences computer architecture Frequency conversion Robustness Frequency diversity pattern recognition

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Perceptual Image Compression using Relativistic Average Least Squares GANs

Perceptual Image Compression using Relativistic Average Leas...

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

作者： Cheng, Zhengxue Fu, Ting Hu, Jiapeng Guo, Li Wang, Shihao Zhao, Xiongxin Zhou, Dajiang Song, Yang Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this work, we provide a detailed description on our submitted methods ANTxNN and ANTxNN SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021. We propose to incorporate Relativistic average Least Squares GANs (RaLSGANs) into Rate-Distortion Optimization for end-to-end training, to achieve perceptual image compression. We also compare two types of discriminator networks and visualize their reconstructed images. Experimental results have validated our method optimized by RaLSGANs can achieve higher subjective quality compared to PSNR, MS-SSIM or LPIPS-optimized models.

关键词： Training Visualization computer vision Image coding conferences Rate-distortion Generative adversarial networks

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Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation

Embedded Discriminative Attention Mechanism for Weakly Super...

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

作者： Wu, Tong Huang, Junshi Gao, Guangyu Wei, Xiaoming Wei, Xiaolin Luo, Xuan Liu, Chi Harold Beijing Inst Technol Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly Supervised Semantic Segmentation (WSSS) with image-level annotation uses class activation maps from the classifier as pseudo-labels for semantic segmentation. However, such activation maps usually highlight the local discriminative regions rather than the whole object, which deviates from the requirement of semantic segmentation. To explore more comprehensive class-specific activation maps, we propose an Embedded Discriminative Attention Mechanism (EDAM) by integrating the activation map generation into the classification network directly for WSSS. Specifically, a Discriminative Activation (DA) layer is designed to explicitly produce a series of normalized class-specific masks, which are then used to generate class-specific pixellevel pseudo-labels demanded in segmentation. For learning the pseudo-labels, the masks are multiplied with the feature maps after the backbone to generate the discriminative activation maps, each of which encodes the specific information of the corresponding category in the input images. Given such class-specific activation maps, a Collaborative Multi-Attention (CMA) module is proposed to extract the collaborative information of each given category from images in a batch. In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation. Based on the generated pseudo-labels, we achieve the mIoU of 70.60% on PASCAL VOC 2012 segmentation test-set, which is the new state-of-the-art, to our best knowledge. Code and pre-trained models are available online soon.

关键词： Image segmentation computer vision Codes Annotations Semantics Collaboration Feature extraction

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Unsupervised Pre-training for Person Re-identification

Unsupervised Pre-training for Person Re-identification

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

作者： Fu, Dengpan Chen, Dongdong Bao, Jianmin Yang, Hao Yuan, Lu Zhang, Lei Li, Houqiang Chen, Dong Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781665445092

In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re-ID datasets are all of limited scale due to the costly effort required for data annotation. Previous research tries to leverage models pre-trained on ImageNet to mitigate the shortage of person Re-ID data but suffers from the large domain gap between ImageNet and person Re-ID data. LUPerson is an unlabeled dataset of 4M images of over 200K identities, which is 30x larger than the largest existing Re-ID dataset. It also covers a much diverse range of capturing environments (e.g., camera settings, scenes, etc.). Based on this dataset, we systematically study the key factors for learning Re-ID features from two perspectives: data augmentation and contrastive loss. Unsupervised pre-training performed on this large-scale dataset effectively leads to a generic Re-ID feature that can benefit all existing person Re-ID methods. Using our pre-trained model in some basic frameworks, our methods achieve state-of-the-art results without bells and whistles on four widely used Re-ID datasets: CUHK03, Market1501, DukeMTMC, and MSMT17. Our results also show that the performance improvement is more significant on small-scale target datasets or under few-shot setting.

关键词： computer vision Annotations Computational modeling Cameras Data models pattern recognition Videos

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Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features

Improving the Efficiency and Robustness of Deepfakes Detecti...

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

作者： Sun, Zekun Han, Yujie Hua, Zeyu Ruan, Na Jia, Weijia Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Beijing Normal Univ BNU Zhuhai Inst Artificial Intelligence & Future Networks Zhuhai Guangdong Peoples R China BNU HKBU United Int Coll Key Lab AI & Multimodal Data Proc Zhuhai Guangdong Peoples R China

ISBN: (纸本)9781665445092

Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusion of information, or even public panic. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a risk of being bypassed by sophisticated manipulation, also resulting high model complexity and sensitiveness to noise. Besides, how to mine the temporal features of manipulated videos and exploit them is still an open question. We propose an efficient and robust framework named LRNet for detecting Deepfakes videos through temporal modeling on precise geometric features. A novel calibration module is devised to enhance the precision of geometric features, making it more discriminative, and a two-stream Recurrent Neural Network (RNN) is constructed for sufficient exploitation of temporal features. Compared to previous methods, our proposed method is lighter-weighted and easier to train. Moreover, our method has shown robustness in detecting highly compressed or noise corrupted videos. Our model achieved 0.999 AUC on FaceForensics+ + dataset. Meanwhile, it has a graceful decline in performance (-0.042 AUC) when faced with highly compressed videos.(1)

关键词： computer vision Recurrent neural networks Feature extraction Robustness pattern recognition Organ transplantation Complexity theory

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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Open-Vocabulary 3D Semantic Segmentation with Foundation Models

Open-Vocabulary 3D Semantic Segmentation with Foundation Mod...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Li Jiang Shaoshuai Shi Bernt Schiele Max Planck Institute for Informatics Saarland Informatics Campus

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In dynamic 3D environments, the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need, we introduce OV3D, an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information, enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features, OV3D empowers open-vocabulary recognition in the 3D domain, achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets, including ScanNet, Matterport3D, and nuScenes.

关键词： computer vision Three-dimensional displays Text recognition Semantic segmentation

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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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Learning Salient Boundary Feature for Anchor-free Temporal Action Localization

Learning Salient Boundary Feature for Anchor-free Temporal A...

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

作者： Lin, Chuming Xu, Chengming Luo, Donghao Wang, Yabiao Tai, Ying Wang, Chengjie Li, Jilin Huang, Feiyue Fu, Yanwei Tencent Youtu Lab Shenzhen Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined anchors and numerous actionness, such methods could be bothered with both large number of outputs and heavy tuning of locations and sizes corresponding to different anchors. Instead, anchor-free methods is lighter, getting rid of redundant hyper-parameters, but gains few attention. In this paper, we propose the first purely anchor-free temporal localization method, which is both efficient and effective. Our model includes (i) an end-to-end trainable basic predictor, (ii) a saliency-based refinement module to gather more valuable boundary features for each proposal with a novel boundary pooling, and (iii) several consistency constraints to make sure our model can find the accurate boundary given arbitrary proposals. Extensive experiments show that our method beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3. Code is available at https://***/TencentYoutuResearch/ActionDetection - AFSD.

关键词： Location awareness computer vision Design methodology Computational modeling Predictive models Feature extraction pattern recognition

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