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检索条件"任意字段=4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021"

共 181 条记录，以下是1-10 订阅

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4th chinese conference on pattern recognition and computer vision, prcv 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, prcv 2021

ISBN: (纸本)9783030880064

the proceedings contain 201 papers. the special focus in this conference is on pattern recognition and computer vision. the topics include: Relation-Guided Actor Attention for Group Activity recognition;MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification;joint Attention Mechanism for Unsupervised Video Object Segmentation;foreground Feature Selection and Alignment for Adaptive Object Detection;exploring Category-Shared and Category-Specific Features for Fine-Grained Image Classification;deep Mixture of Adversarial Autoencoders Clustering Network;SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization;conditioners for Adaptive Regression Tracking;attention Template Update Model for Siamese Tracker;3D Multi-object Detection and Tracking with Sparse Stationary LiDAR;insight on Attention Modules for Skeleton-Based Action recognition;AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context;two-Stage recognition Algorithm for Untrimmed Converter Steelmaking Flame Video;scale-Aware Multi-branch Decoder for Salient Object Detection;densely End Face Detection Network for Counting Bundled Steel Bars Based on YoloV5;POT: A Dataset of Panoramic Object Tracking;DP-YOLOv5: computer vision-Based Risk Behavior Detection in Power Grids;distillation-Based Multi-exit Fully Convolutional Network for Visual Tracking;handwriting Trajectory Reconstruction Using Spatial-Temporal Encoder-Decoder Network;scene Semantic Guidance for Object Detection;CRNet: Centroid Radiation Network for Temporal Action Localization;training Person Re-identification Networks with Transferred Images;ACFIM: Adaptively Cyclic Feature Information-Interaction Model for Object Detection;research of Robust Video Object Tracking Algorithm Based on Jetson Nano Embedded Platform;classification-IoU Joint Label Assignment for End-to-End Object Detection;joint Learning Appearance and Motion Models for Visual Tracking;reFl

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4th chinese conference on pattern recognition and computer vision, prcv 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, prcv 2021

ISBN: (纸本)9783030880033

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4th chinese conference on pattern recognition and computer vision, prcv 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, prcv 2021

ISBN: (纸本)9783030880095

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4th chinese conference on pattern recognition and computer vision, prcv 2021

4th Chinese Conference on Pattern Recognition and Computer V...

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4th chinese conference on pattern recognition and computer vision, prcv 2021

ISBN: (纸本)9783030880125

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BEVDot: Enhancing Environmental Perception for Autonomous Driving with a Deformable Depth Mechanism 7th

BEVDot: Enhancing Environmental Perception for Autonomous Dr...

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7th chinese conference on pattern recognition and computer vision

作者： Yang, Chunmeng Lai, Zeyu Lu, Gaofeng Kong, Bin Univ Sci & Technol China Hefei 230026 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Anhui Engn Res Ctr Intelligent Driving Technol & Hefei 230031 Peoples R China

ISBN: (纸本)9789819787913;9789819787920

In the field of autonomous driving, environmental perception is crucial for driving safety. Addressing the limitations of existing visual perception methods in complex scenarios, this study proposes a deformable depth visual perception framework based on a multi-camera system. the framework processes multi-camera data through a feature extraction network to generate and fuse multi-scale features. And a deformable depth prediction mechanism incorporating self-vehicle temporal difference features is introduced to enhance the accuracy of the model in depth prediction. Experimental results show that on the NuScenes dataset, our method achieves a detection accuracy (mAP) of 0.508 using only 5 random cameras out of 6, surpassing existing technologies such as Lift-Splat (0.446), RC-BEVFusion (0.476), and SOGDet-SE (0.474). Future research will focus on improving the prediction accuracy of distant vehicles to further enhance the performance of the model.

关键词： Automated Driving Visual Perception Vehicle Detection

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Multi-modal Knowledge-Enhanced Fine-Grained Image Classification 7th

Multi-modal Knowledge-Enhanced Fine-Grained Image Classifica...

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7th chinese conference on pattern recognition and computer vision

作者： Cheng, Suyan Zhang, Feifei Zhou, Haoliang Xu, Changsheng Tianjin Univ Technol Tianjin Peoples R China Jiangsu Univ Sci & Technol Zhenjiang Jiangsu Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9789819786190;9789819786206

In image classification tasks, visual appearance is generally considered as a crucial cue for understanding images. However, relying solely on visual information can lead to misclassification in fine-grained image classification tasks. Multi-modal knowledge has been proven to provide critical cues for various computer vision tasks, such as image retrieval and vision question answering. In this paper, we integrate multi-modal knowledge into visual features to enhance the model's understanding of visual content and accomplish fine-grained image classification tasks. Specifically, we adopt an effective visual enhanced module to capture global and local features, obtaining discriminative visual representations. Meanwhile, we employ knowledge distillation to transfer multi-modal knowledge from the Contrastive Language-Image Pretraining (CLIP) model to our model, improving its generalization ability. Moreover, we incorporate scene text into our visual features to provide richer contextual information. Experiments on the Con-Text, Drink Bottle, and Crowd Activity benchmark datasets demonstrate that our approach achieves 5.41%, 1.2%, and 7.55% improvements in mAP compared to the current state-of-the-art methods, respectively.

关键词： Fine-grained image classification vision transformer Scene text

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SDFReg: Learning Signed Distance Functions for Point Cloud Registration 7th

SDFReg: Learning Signed Distance Functions for Point Cloud R...

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7th chinese conference on pattern recognition and computer vision

作者： Zhang, Leida Lu, Zhengda Liu, Kai Wang, Yiqun Chongqing Univ Coll Comp Sci Chongqing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chongqing Univ Natl Elite Inst Engn Chongqing Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds. To this end, we propose a novel point cloud registration framework for these imperfect point clouds. By introducing a neural implicit representation, we replace the problem of rigid registration between point clouds with a registration problem between the point cloud and the neural implicit function. We then propose to alternately optimize the implicit function and the registration between the implicit function and point cloud. In this way, point cloud registration can be performed in a coarse-to-fine manner. By fully capitalizing on the capabilities of the neural implicit function without computing point correspondences, our method showcases remarkable robustness in the face of challenges such as noise, incompleteness, and density changes of point clouds.

关键词： Point cloud registration Signed distance function Neural implicit representation

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F4SR: A Feed-Forward Regression Approach for Few-Shot Face Super-Resolution 7th

F4SR: A Feed-Forward Regression Approach for Few-Shot Face S...

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7th chinese conference on pattern recognition and computer vision

作者： Fu, Jican Jiang, Kui Liu, Xianming Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9789819786848;9789819786855

this paper presents a novel approach to face super-resolution that explicitly models the relationship between low-resolution and high-resolution images. Unlike many existing methods, the proposed approach does not require a large number of high-resolution and low-resolution image pairs for training, making it applicable in scenarios with limited training data. By utilizing a feed-forward regression model, the proposed method provides a more interpretable and transparent approach to face super-resolution, enhancing the explainability of the super-resolution process. In particular, by progressively exploiting the contextual information of local patch, the proposed feed-forward regression method can model the relationship between the low-resolution and high-resolution images within a large receptive field. this is somewhat similar to the idea of convolutional neural networks, but the proposed approach is completely interpretable. Experimental results demonstrate that the proposed method achieves good performance in generating high-resolution face images, outperforming several existing methods. Overall, the proposed method contributes to advancing the field of face super-resolution by introducing a more interpretable and transparent approach that can achieve good results with minimal training data.

关键词： Face Hallucination Image Super-Resolution Feed-Forward Regression

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Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-Trained Models 7th

Adapt and Refine: A Few-Shot Class-Incremental Learner via P...

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7th chinese conference on pattern recognition and computer vision

作者： Qiang, Sunyuan Xiong, Zhu Liang, Yanyan Wan, Jun Zhang, Du Macau Univ Sci & Technol Macau Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci SAI Beijing Peoples R China

ISBN: (纸本)9789819784868;9789819784875

the intricate and ever-changing nature of the real world imposes greater demands on neural networks, necessitating the rapid assimilation of fleeting new concepts as they arise. Consequently, a novel learning paradigm has emerged, namely, few-shot class-incremental learning (FSCIL), which aims to continuously update knowledge of novel categories with insufficient instances while avoiding catastrophic forgetting of previous knowledge. However, recent FSCIL methods encountered significant performance limitations due to the low-quality latent representation spaces obtained from base session. To this end, this paper introduces a novel FSCIL method, Adapt and REfine (ARE). Specifically, ARE initially strengthens the latent space through the powerful representational capabilities of pre-trained models (PTMs). Subsequently, we further adapt and refine the feature space and prototypes to promote the enhancement of FSCIL performance. Extensive experiments on benchmarks such as CIFAR100, mini-ImageNet, and CUB200 validate the effectiveness of the proposed method.

关键词： Few-shot class-incremental learning (FSCIL) center dot Catastrophic forgetting Pre-trained models (PTMs)

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ST-SBV: Spatial-Temporal Self-Blended Videos for Deepfake Detection 7th

ST-SBV: Spatial-Temporal Self-Blended Videos for Deepfake De...

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7th chinese conference on pattern recognition and computer vision

作者： Guan, Weinan Wang, Wei Peng, Bo Dong, Jing Tan, Tieniu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China NLPR CASIA Beijing Peoples R China

ISBN: (纸本)9789819786190;9789819786206

the generalization ability to unseen forgery data is a critical concern in deepfake detection tasks. In this paper, we propose a novel spatial-temporal generation method for synthesizing forgery data, called spatial-temporal self-blended video (ST-SBV). ST-SBV is specifically designed to train deepfake detectors in a self-supervised manner. We utilize various image augmentation techniques on a genuine video to create pseudo source and target videos. Subsequently, ST-SBV is generated by blending them using face masks, following the generation process of a deepfake video. the key idea behind ST-SBV is to imitate the spatial and temporal artifacts in deepfake videos that are inherent and generalizable across deepfake generation methods. this encourages detectors to learn more general and effective representations. Specially, compared to existing data synthesis methods, ST-SBV addresses the gap in investigating spatial-temporal inconsistencies in forgery videos. Extensive experiments demonstrate that our method significantly improves the generalization performance of deepfake detectors on unknown forgery data. In particular, on the challenging datasets DFDCP and DFDC, our method outperforms the baselines by 5.20% and 4.94%, respectively.

关键词： Deepfake detection Synthetic forgery data

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