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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是391-400 订阅

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A Two-Stage Shake-Shake Network for Long-Tailed recognition of SAR Aerial View Objects

A Two-Stage Shake-Shake Network for Long-Tailed Recognition ...

引用

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

作者： Li, Gongzhe Pan, Linpeng Qiu, Linwei Tan, Zhiwen Xie, Fengying Zhang, Haopeng Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity limit the performance of classification methods, especially for the data-sensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and the classification with alternating normalization (CAN) to improve the accuracy. In the PBVS 1 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which wins the top-tier among all the participants.

关键词： Training Representation learning Feature extraction Radar tracking Radar polarimetry pattern recognition Task analysis

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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR S...

引用

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

作者： Kalinicheva, Ekaterina Landrieu, Loic Mallet, Clement Chehata, Nesrine Univ Gustave Eiffel LASTIG IGN ENSG F-77454 Marne La Vallee France Univ Bordeaux BIOGECO UMR 1202 INRAE Bordeaux France Univ Bordeaux Montaigne Bordeaux INP Bordeaux France

ISBN: (纸本)9781665487399

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m(2) with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D pointwise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://***/ekalinicheva/multi_layer_vegetation.

关键词： computer vision Solid modeling Three-dimensional displays Laser radar Annotations Atmospheric modeling Time series analysis

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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression recognition

Coarse-to-Fine Cascaded Networks with Smooth Predicting for ...

引用

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

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT.

关键词： Human computer interaction computer vision Codes Face recognition conferences Behavioral sciences

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TDT: Teaching Detectors to Track without Fully Annotated Videos

TDT: Teaching Detectors to Track without Fully Annotated Vid...

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

作者： Yu, Shuzhi Wu, Guanhang Gu, Chunhui Fathy, Mohammed E. Duke Univ Durham NC 27706 USA Google LLC Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) benchmarks. However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain. This can limit the model generalization. In comparison, the two-stage approach, which performs detection and embedding separately, is slower but easier to train as their data are easier to annotate. We propose to combine the best of the two worlds through a data distillation approach. Specifically, we use a teacher embedder, trained on Re-ID datasets, to generate pseudo appearance embedding labels for the detection datasets. Then, we use the augmented dataset to train a detector that is also capable of regressing these pseudo-embeddings in a fully-convolutional fashion. Our proposed one-stage solution matches the two-stage counterpart in quality but is 3 times faster. Even though the teacher embedder has not seen any tracking data during training, our proposed tracker achieves competitive performance with some popular trackers (e.g. JDE) trained with fully labeled tracking data.

关键词： Training computer vision Annotations conferences Detectors Predictive models Benchmark testing

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

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

作者： Jain, Himalaya Tuan-Hung Vu Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9781665487399

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains;ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions

Three Stream Graph Attention Network using Dynamic Patch Sel...

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

作者： Jain, Ankith Kumar, Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.

关键词： Emotion recognition computer vision Face recognition conferences Feature extraction Skin Facial muscles

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Video-based Frame-level Facial Analysis of Affective Behavior on Mobile Devices using EfficientNets

Video-based Frame-level Facial Analysis of Affective Behavio...

引用

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

作者： Savchenko, Andrey, V HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. The predictions for sequential frames are smoothed using mean or median filters. It is demonstrated that our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale AffWild2 database from the third Affective Behavior Analysis in-the-wild Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.1-0.5 higher performance measures for test sets in the uni-task Expression Classification, Valence-Arousal Estimation, Action Unit Detection and Multi-Task Learning. Our team took the 3rd place in the multi-task learning challenge and 4th places in Valence-Arousal and Expression challenges. Due to simplicity, the proposed approach may be considered as a new baseline for all four sub-challenges.

关键词： Emotion recognition Analytical models Computational modeling Multitasking Feature extraction Real-time systems Mobile handsets

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Dark Corner on Skin Lesion Image Dataset: Does it matter?

Dark Corner on Skin Lesion Image Dataset: Does it matter?

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

作者： Pewton, Samuel William Yap, Moi Hoon Manchester Metropolitan Univ Dept Comp & Math John Dalton Bldg Manchester M1 5GD Lancs England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Skin lesion image datasets gained popularity in recent years with the successes of ISIC datasets and challenges. While the users of these datasets are growing, the Dark Corner Artifact (DCA) phenomenon is under explored. This paper provides a better understanding of how and why DCA occurs, the types of DCAs and investigates the DCA within a curated ISIC image dataset. We introduce new labels of image artifacts on a curated balanced dataset of 9,810 images and identified 2,631 images with different intensities of DCA. Then, we improve the quality of this dataset by introducing automated DCA detection and removal methods. We evaluate the performance of our methods with image quality metrics on an unseen dataset (Dermofit), and achieved better SSIM score in every DCA intensity level. Further, we study the effects of DCA removal on a binary classification task (melanoma vs non-melanoma). Although deep learning performances in this task show marginal differences, we demonstrate that with DCA removal, it can help to shift the network activations to the skin lesions. All the artifact labels and codes are available at: https://***/mmu-dermatologyresearch/dark_corner_artifact_removal.

关键词： Deep learning Measurement Image quality computer vision Image edge detection Melanoma Skin

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Network Amplification with Efficient MACs Allocation

Network Amplification with Efficient MACs Allocation

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

作者： Liu, Chuanjian Han, Kai Xiao, An Nie, Ying Zhang, Wei Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracies, such as EfficientNet and RegNet. These works try to enlarge the network architecture with one unified rule by sampling and statistical methods. However, the rule is not prospective to the design of large networks because it is obtained from the experience of researchers on small network architectures. In this paper, we propose to enlarge the capacity of CNN models by fine-grained MACs allocation for the width, depth and resolution on the stage level. In particular, starting from a base small model, we gradually add extra channels, layers or resolution by using a dynamic programming manner. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. On EfficientNet, our method consistently outperforms the performance of the original scaling method. In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies under the setting of 600M and 4.4B MACs, respectively.

关键词： Statistical analysis Computational modeling Heuristic algorithms Neural networks Network architecture pattern recognition Dynamic programming

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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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