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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是171-180 订阅

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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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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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ALPS: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS

ALPS: Adaptive Quantization of Deep Neural Networks with Gen...

引用

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

作者： Langroudi, Hamed F. Karia, Vedant Carmichael, Zachariah Zyarah, Abdullah Pandit, Tej Gustafson, John L. Kudithipudi, Dhireesha Univ Texas San Antonio Neuromorph AI Lab San Antonio TX 78249 USA Rochester Inst Technol Rochester NY 14623 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, a new adaptive quantization algorithm for generalized posit format is presented, to optimally represent the dynamic range and distribution of deep neural network parameters. Adaptation is achieved by minimizing the intra-layer posit quantization error with a compander. The efficacy of the proposed quantization algorithm is studied within a new low-precision framework, ALPS, on ResNet-50 and EfficientNet models for classification tasks. Results assert that the accuracy and energy dissipation of low-precision DNNs using generalized posits outperform other well-known numerical formats, including standard posits.

关键词： Deep learning Quantization (signal) Adaptive systems Upper bound Numerical analysis Heuristic algorithms pattern recognition

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Edge Guided Progressively Generative Image Outpainting

Edge Guided Progressively Generative Image Outpainting

引用

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

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision Image edge detection Computational modeling Image processing conferences Semantics

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Alleviating Representational Shift for Continual Fine-tuning

Alleviating Representational Shift for Continual Fine-tuning

引用

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

作者： Jie, Shibo Deng, Zhi-Hong Li, Ziheng Peking Univ Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers' representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normalization (Xconv BN) and hierarchical fine-tuning. Xconv BN maintains pre-convolution running means instead of post-convolution, and recovers post-convolution ones before testing, which corrects the inaccurate estimates of means under IRS. Hierarchical fine-tuning leverages a multi-stage strategy to fine-tune the pre-trained network, preventing massive changes in Conv layers and thus alleviating IRS. Experimental results on four datasets show that our method remarkably outperforms several state-of-the-art methods with lower storage overhead.

关键词： Training computer vision conferences pattern recognition Task analysis Testing

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Essentials for Class Incremental Learning

Essentials for Class Incremental Learning

引用

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

作者： Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665448994

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.

关键词： Learning systems computer vision Art conferences Neural networks Training data Boosting

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

引用

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

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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Instagram Filter Removal on Fashionable Images

Instagram Filter Removal on Fashionable Images

引用

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

作者： Kinli, Furkan Ozcan, Baris Kirac, Furkan Ozyegin Univ Video Vis & Graph Lab Istanbul Turkey

ISBN: (纸本)9781665448994

Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.

关键词： computer vision Image color analysis Social networking (online) conferences Multimedia Web sites Estimation Information filters

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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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SAM: The Sensitivity of Attribution Methods to Hyperparameters

SAM: The Sensitivity of Attribution Methods to Hyperparamete...

引用

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

作者： Bansal, Naman Agarwal, Chirag Anh Nguyen Auburn Univ Auburn AL 36849 USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781728193601

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

关键词： computer vision

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