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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Unleashing Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

Unleashing Potential of Unsupervised Pre-Training with Intra...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zizheng Jin, Xin Zheng, Kecheng Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing person re-identification (ReID) methods typically load the pre-trained ImageNet weights for initialization directly. However, as a fine-grained classification task, ReID is more challenging and there exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pre-training framework for re-identification (UP-ReID) based on the contrastive learning (CL) pipeline. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in the CL pipeline usually distort the discriminative clues in person images, and (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an infra-identity (I-2-)regularization in the UP-ReID, which is instantiated as two constraints coining from the global image and local patch aspects, respectively. A global consistency constraint is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular Re-ID datasets, PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pre-trained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance.

关键词： Representation learning computer vision Pipelines Self-supervised learning Robustness pattern recognition Task analysis

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Deep Generalized Unfolding Networks for Image Restoration

Deep Generalized Unfolding Networks for Image Restoration

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mou, Chong Wang, Qian Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep neural networks (DNN) have achieved great success in image restoration. However, most DNN methods are designed as a black box, lacking transparency and interpretability. Although some methods are proposed to combine traditional optimization algorithms with DNN, they usually demand pre-defined degradation processes or handcrafted assumptions, making it difficult to deal with complex and real-world applications. In this paper, we propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation. In addition, we design inter-stage information pathways across proximal mapping in different PGD iterations to rectify the intrinsic information loss in most deep unfolding networks (DUN) through a multi-scale and spatial-adaptive way. By integrating the flexible gradient descent and informative proximal mapping, we unfold the iterative PGD algorithm into a trainable DNN. Extensive experiments on various image restoration tasks demonstrate the superiority of our method in terms of state-of-theart performance, interpretability, and generalizability.

关键词： Degradation Deep learning computer vision Neural networks Estimation Iterative algorithms Image restoration

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A functional approach to rotation equivariant non-linearities for Tensor Field Networks

A functional approach to rotation equivariant non-linearitie...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Poulenard, Adrien Guibas, Leonidas J. Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Learning pose invariant representation is a fundamental problem in shape analysis. Most existing deep learning algorithms for 3D shape analysis are not robust to rotations and are often trained on synthetic datasets consisting of pre-aligned shapes, yielding poor generalization to unseen poses. This observation motivates a growing interest in rotation invariant and equivariant methods. The field of rotation equivariant deep learning is developing in recent years thanks to a well established theory of Lie group representations and convolutions. A fundamental problem in equivariant deep learning is to design activation functions which are both informative and preserve equivariance. The recently introduced Tensor Field Network (TFN) framework provides a rotation equivariant network design for point cloud analysis. TFN features undergo a rotation in feature space given a rotation of the input pointcloud. TFN and similar designs consider nonlinearities which operate only over rotation invariant features such as the norm of equivariant features to preserve equivariance, making them unable to capture the directional information. In a recent work entitled "Gauge Equivariant Mesh CNNs: Anisotropic Convolutions on Geometric Graphs" Hann et al. interpret 2D rotation equivariant features as Fourier coefficients of functions on the circle. In this work we transpose the idea of Hann et al. to 3D by interpreting TFN features as spherical harmonics coefficients of functions on the sphere. We introduce a new equivariant nonlinearity and pooling for TFN. We show improvments over the original TFN design and other equivariant nonlinearities in classification and segmentation tasks. Furthermore our method is competitive with state of the art rotation invariant methods in some instances.

关键词： Deep learning computer vision Three-dimensional displays Tensors Shape Harmonic analysis pattern recognition

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Nearest Neighbor Matching for Deep Clustering

Nearest Neighbor Matching for Deep Clustering

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dang, Zhiyuan Deng, Cheng Yang, Xu Wei, Kun Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China JD Tech Beijing 100176 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relationships may construct more semantically confident sample pairs, leading to inferior performance. To tackle this issue, we propose a method called Nearest Neighbor Matching (NNM) to match samples with their nearest neighbors from both local (batch) and global (overall) levels. Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features. To keep the clustering assignment consistent in both neighbors and classes, we frame consistent loss and class contrastive loss for both local and global levels. Experimental results on three benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods. Particularly on the STL-10 dataset, our method can achieve supervised performance. As for the CIFAR-100 dataset, our NNM leads 3.7% against the latest comparison method. Our code will be available at https://***/ZhiyuanDang/NNM.

关键词： computer vision Codes Semantics Artificial neural networks Benchmark testing pattern recognition Unsupervised learning

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Deblur-NeRF: Neural Radiance Fields from Blurry Images

Deblur-NeRF: Neural Radiance Fields from Blurry Images

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ma, Li Li, Xiaoyu Liao, Jing Zhang, Qi Wang, Xuan Wang, Jue Sander, Pedro, V Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tencent AI Lab Bellevue WA USA City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural Radiance Field (NeRF) has gained considerable attention recently for 3D scene reconstruction and novel view synthesis due to its remarkable synthesis quality. However, image blurriness caused by defocus or motion, which often occurs when capturing scenes in the wild, significantly degrades its reconstruction quality. To address this problem, We propose Deblur-NeRF, the first method that can recover a sharp NeRF from blurry input. We adopt an analysis-by-synthesis approach that reconstructs blurry views by simulating the blurring process, thus making NeRF robust to blurry inputs. The core of this simulation is a novel Deformable Sparse Kernel (DSK) module that models spatially-varying blur kernels by deforming a canonical sparse kernel at each spatial location. The ray origin of each kernel point is jointly optimized, inspired by the physical blurring process. This module is parameterized as an MLP that has the ability to be generalized to various blur types. Jointly optimizing the NeRF and the DSK module allows us to restore a sharp NeRF. We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes. Evaluation results on both synthetic and real-world data show that our method outperforms several baselines. The synthetic and real datasets along with the source code is publicly available at https: //***/deblurnerf/.

关键词： Deformable models Training Photography computer vision Three-dimensional displays Sensors pattern recognition

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Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

Context-aware Biaffine Localizing Network for Temporal Sente...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Daizong Qu, Xiaoye Dong, Jianfeng Zhou, Pan Cheng, Yu Wei, Wei Xu, Zichuan Xie, Yulai Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huawei Cloud Shenzhen Peoples R China Zhejiang Gongshang Univ Hangzhou Zhejiang Peoples R China Microsoft AI Redmond WA USA Dalian Univ Technol Dalian Liaoning Peoples R China

ISBN: (纸本)9781665445092

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. Previous works either compare pre-defined candidate segments with the query and select the best one by ranking, or directly regress the boundary timestamps of the target segment. In this paper, we propose a novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism. In particular, we present a Contextaware Biaffine Localizing Network (CBLN) which incorporates both local and global contexts into features of each start/end position for biaffine-based localization. The local contexts from the adjacent frames help distinguish the visually similar appearance, and the global contexts from the entire video contribute to reasoning the temporal relation. Besides, we also develop a multi-modal self-attention module to provide fine-grained query-guided video representation for this biaffine strategy. Extensive experiments show that our CBLN significantly outperforms state-of-thearts on three public datasets (ActivityNet Captions, TACoS, and Charades-STA), demonstrating the effectiveness of the proposed localization framework. The code is available at https://***/liudaizong/CBLN.

关键词： Location awareness computer vision Codes Grounding Cognition pattern recognition Task analysis

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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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Stratified Transformer for 3D Point Cloud Segmentation

Stratified Transformer for 3D Point Cloud Segmentation

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lai, Xin Liu, Jianhui Jiang, Li Wang, Liwei Zhao, Hengshuang Liu, Shu Qi, Xiaojuan Jia, Jiaya CUHK Hong Kong Peoples R China HKU Hong Kong Peoples R China SmartMore Hong Kong Peoples R China MPI Informat Saarbrucken Germany MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance. Specifically, we first put forward a novel key sampling strategy. For each query point, we sample nearby points densely and distant points sparsely as its keys in a stratified way, which enables the model to enlarge the effective receptive field and enjoy long-range contexts at a low computational cost. Also, to combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance. Besides, we adopt contextual relative position encoding to adaptively capture position information. Finally, a memory-efficient implementation is introduced to overcome the issue of varying point numbers in each window Extensive experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code is available at https://***/dvlab-research/Stratified-Transformer.

关键词： Point cloud compression computer vision Three-dimensional displays Transformers Encoding Computational efficiency pattern recognition

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LAR-SR: A Local Autoregressive Model for Image Super-Resolution

LAR-SR: A Local Autoregressive Model for Image Super-Resolut...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Guo, Baisong Zhang, Xiaoyun Wu, Haoning Wang, Yu Zhang, Ya Wang, Yan-Feng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic images. As another powerful generative approach, autoregressive (AR) model has not been noticed in low level tasks due to its limitation. Based on the fact that given the structural information, the textural details in the natural images are locally related without long term dependency, in this paper we propose a novel autoregressive model-based SR approach, namely LAR-SR, which can efficiently generate realistic SR images using a novel local autoregressive (LAR) module. The proposed LAR module can sample all the patches of textural components in parallel, which greatly reduces the time consumption. In addition to high time efficiency, it is also able to leverage contextual information of pixels and can be optimized with a consistent loss. Experimental results on the widely-used datasets show that the proposed LAR-SR approach achieves superior performance on the visual quality and quantitative metrics compared with other generative models such as GAN, Flow, and is competitive with the mixture generative model.

关键词： Measurement Adaptation models computer vision Computational modeling Superresolution pattern recognition Image restoration

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Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order Consistency

Set-Supervised Action Learning in Procedural Task Videos via...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lu, Zijia Elhamifar, Ehsan Northeastern Univ Boston MA 02115 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We address the problem of set-supervised action learning, whose goal is to learn an action segmentation model using weak supervision in the form of sets of actions occurring in training videos. Our key observation is that videos within the same task have similar ordering of actions, which can be leveraged for effective learning. Therefore, we propose an attention-based method with a new Pairwise Ordering Consistency (POC) loss that encourages that for each common action pair in two videos of the same task, the attentions of actions follow a similar ordering. Unlike existing sequence alignment methods, which misalign actions in videos with different orderings or cannot reliably separate more from less consistent orderings, our POC loss efficiently aligns videos with different action orders and is differentiable, which enables end-to-end training. In addition, it avoids the time-consuming pseudo-label generation of prior works. Our method efficiently learns the actions and their temporal locations, therefore, extends the existing attention-based action localization methods from learning one action per video to multiple actions using our POC loss along with video-level and frame-level losses. By experiments on three datasets, we demonstrate that our method significantly improves the state of the art. We also show that our method, with a small modification, can effectively address the transcript-supervised action learning task, where actions and their ordering are available during training.(1)

关键词： Training Location awareness computer vision Shape pattern recognition Reliability Task analysis

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