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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是51-60 订阅

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Super-Resolution based Video Coding Scheme

Super-Resolution based Video Coding Scheme

引用

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

作者： Cho, Hyun Min Choi, Kiho Gacheon Univ Sch Comp 1342 Seongnamdaero Seongnam Si Gyeonggi Do South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we present a super-resolution-based video coding scheme that compresses video data by combining traditional hybrid video coding and Convolutional neural network-based video coding. During video encoding, downsampling reduces the resolution of an original video in both horizontal and vertical directions to reduce original video data, and Convolutional neural networkbased super-resolution is employed after the decoding process to recover the resolution of the reconstructed video during upsampling. For core encoding and decoding processes, the latest video coding standard (i.e., VVC/H.266) is conducted. The experimental results show that the proposed method can provide efficient coding performance while maintaining good visual quality.

关键词： Video coding Visualization Image coding Superresolution Encoding Decoding pattern recognition

来源：评论

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Self Supervised Scanpath Prediction Framework for Painting Images

Self Supervised Scanpath Prediction Framework for Painting I...

引用

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

作者： Tliba, Marouane Kerkouri, Mohamed Amine Chetouani, Aladine Bruno, Alessandro Univ Orleans Orleans France Bournemouth Univ Poole Dorset England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiveness of our approach, we firstly propose a lightweight scanpath baseline model and compare its performance to some state-of-the-art methods. Secondly, we train the encoder of our baseline model on large-scale painting images to study the efficiency of the proposed self-supervised strategy. The lightweight decoder proves effective in learning from the self-supervised pre-trained encoder with better performances than the end-to-end fine-tuned supervised baseline on two painting datasets, including a proposed new visual attention modelling dataset.

关键词： Visualization Self-supervised learning Predictive models Observers Quality assessment pattern recognition Task analysis

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

引用

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

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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Self-Supervised Variable Rate Image Compression using Visual Attention

Self-Supervised Variable Rate Image Compression using Visual...

引用

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad Gujarat India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The recent success of self-supervised learning relies on its ability to learn the representations from self-defined pseudo-labels that are applied to several downstream tasks. Motivated by this ability, we present a deep image compression technique, which learns the lossy reconstruction of raw images from the self-supervised learned representation of SimCLR ResNet-50 architecture. Our framework uses a feature pyramid to achieve the variable rate compression of the image using a self-attention map for the optimal allocation of bits. The paper provides an overview to observe the effects of contrastive self-supervised representations and the self-attention map on the distortion and perceptual quality of the reconstructed image. The experiments are performed on a different class of images to show that the proposed method outperforms the other variable rate deep compression models without compromising the perceptual quality of the images.

关键词： Visualization Image coding Image analysis conferences Self-supervised learning Distortion pattern recognition

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HMIway-env: A Framework for Simulating Behaviors and Preferences to Support Human-AI Teaming in Driving

HMIway-env: A Framework for Simulating Behaviors and Prefere...

引用

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

作者： Gopinath, Deepak DeCastro, Jonathan Rosman, Guy Sumner, Emily Morgan, Allison Hakimi, Shabnam Stent, Simon Toyota Res Inst Los Altos CA 94022 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We introduce a lightweight simulation and modeling framework, HMIway-env, for studying human-machine teaming in the context of driving. The goal of the framework is to accelerate the development of adaptive AI systems which can respond to individual driver states, traits, and preferences, by serving as a data-generation engine and training environment for learning personalized human-AI teaming policies. We extend highway-env, an OpenAI Gym-based simulator environment, to enable specification of human driver behavior, and design of vehicle-driver interactions and outcomes. We describe one instance of our framework incorporating models for distracted and cautious driving, which we validate through crowd-sourced feedback, and show early experimental results toward the training of better intervention policies.

关键词： Training Adaptation models Fatigue Behavioral sciences pattern recognition Artificial intelligence Task analysis

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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

引用

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

作者： Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

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Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval

Multi-view Multi-label Canonical Correlation Analysis for Cr...

引用

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

作者： Sanghavi, Rushil Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a generalization of CCA for multi-view data that also makes use of high-level semantic information available in the form of multi-label annotations in each view. While CCA relies on explicit pairings/associations of samples between two views (or modalities), MVMLCCA uses the available multi-label annotations to establish correspondence across multiple (two or more) views without the need of explicit pairing of multi-view samples. Extensive experiments on two multi-modal datasets demonstrate that the proposed approach offers much more flexibility than the related approaches without compromising on scalability and cross-modal retrieval performance. Our code and precomputed features are available at https://***/Rushil231100/MVMLCCA.

关键词： computer vision Correlation Codes Annotations Scalability conferences Semantics

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

引用

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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Multi-Camera Vehicle Tracking System for AI City Challenge 2022

Multi-Camera Vehicle Tracking System for AI City Challenge 2...

引用

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

作者： Li, Fei Wang, Zhen Nie, Ding Zhang, Shiyi Jiang, Xingqun Zhao, Xingxing Hu, Peng BOE Technol Grp Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-Target Multi-Camera tracking is a fundamental task for intelligent traffic systems. The track 1 of AI City Challenge 2022 aims at the city-scale multi-camera vehicle tracking task. In this paper we propose an accurate vehicle tracking system composed of 4 parts, including: (1) State-of-the-art detection and re-identification models for vehicle detection and feature extraction. (2) Single camera tracking, where we introduce augmented tracks prediction and multi-level association method on top of tracking-by-detection paradigm.(3) Zone-based singe-camera track-let merging strategy. (4) Multi-camera spatial-temporal matching and clustering strategy. The proposed system achieves promising results and ranks the second place in Track 1 of the AI City Challenge 2022 with a IDF1 score of 0.8437.

关键词： computer vision Vehicle detection conferences Urban areas Merging Predictive models Feature extraction

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Temporal Driver Action Localization using Action Classification Methods

Temporal Driver Action Localization using Action Classificat...

引用

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

作者： Alyahya, Munirah Alghannam, Shahad Alhussan, Taghreed Saudi Technol & Secur Comprehens Control Co Riyadh Saudi Arabia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end time of a given driver action. The TDAL framework consists of three stages: preprocessing, 'which takes untrimmed video as input and generates multiple clips;action classification, which classifies the clips;and finally, the classifier output is sent to the temporal action localization to generate the start and end times of the distracted actions. The proposed framework achieves an F1 score of 27.06% on Track 3A2 dataset of NVIDIA AI City 2022 Challenge. The findings show that the TDAL framework contributes to fine-grained driver distraction recognition and paves the way for the development of smart and safe transportation. Code will be available soon.

关键词： Location awareness computer vision Urban areas Data preprocessing Transportation Classification algorithms Safety

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