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Cross-view Transformers for real-time Map-view Semantic Segmentation

Cross-view Transformers for real-time Map-view Semantic Segm...

引用

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

作者： Zhou, Brady Krahenbuhl, Philipp UT Austin Austin TX 78712 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present cross-view transformers, an efficientattention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in realtime. The presented architecture performs at state-of-theart on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://***/bradyz/ cross_view_transformers.

关键词： Convolutional codes Image segmentation computer vision Navigation Semantics computer architecture Transformers

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FAM: Visual Explanations for the Feature Representations from Deep Convolutional Networks

FAM: Visual Explanations for the Feature Representations fro...

引用

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

作者： Wu, Yuxi Chen, Changhuai Che, Jun Pu, Shiliang Hikvis Res Inst Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In recent years, increasing attention has been drawn to the internal mechanisms of representation models. Traditional methods are inapplicable to fully explain the feature representations, especially if the images do not fit into any category. In this case, employing an existing class or the similarity with other image is unable to provide a complete and reliable visual explanation. To handle this task, we propose a novel visual explanation paradigm called Feature Activation Mapping (FAM) in this paper. Under this paradigm, Grad-FAM and Score-FAM are designed for visualizing feature representations. Unlike the previous approaches, FAM locates the regions of images that contribute most to the feature vector itself. Extensive experiments and evaluations, both subjective and objective, showed that Score-FAM provided most promising interpretable visual explanations for feature representations in Person Re-Identification. Furthermore, FAM also can be employed to analyze other vision tasks, such as self-supervised representation learning.

关键词： Representation learning Visualization computer vision Privacy Ethics Neurons Feature extraction

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PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models

PCA-Based Knowledge Distillation Towards Lightweight and Con...

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

作者： Chiu, Tai-Yin Gurari, Danna Univ Texas Austin Austin TX 78712 USA Univ Colorado Boulder CO 80309 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the observation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by theory. To our knowledge, this is the first knowledge distillation method for photorealistic style transfer. Our experiments demonstrate its versatility for use with different backbone architectures, VGG and MobileNet, across six image resolutions. Compared to existing models, our top-performing model runs at speeds 5-20x faster using at most 1% of the parameters. Additionally, our distilled models achieve a better balance between stylization strength and content preservation than existing models. To support reproducing our method and models, we share the code at https://***/chiutaiyin/PCA-Knowledge-Distillation.

关键词： Deep learning computer vision Image resolution Codes Computational modeling computer architecture pattern recognition

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CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segme...

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

作者： Yu, Qihang Wang, Huiyu Kim, Dahun Qiao, Siyuan Collins, Maxwell Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Korea Adv Inst Sci & Technol Daejeon South Korea Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering. It rethinks the existing transformer architectures used in segmentation and detection;CMT-DeepLab considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation. The clustering is computed with an alternating procedure, by first assigning pixels to the clusters by their feature affinity, and then updating the cluster centers and pixel features. Together, these operations comprise the Clustering Mask Transformer (CMT) layer, which produces cross-attention that is denser and more consistent with the final segmentation task. CMT-DeepLab improves the performance over prior art significantly by 4.4% PQ, achieving a new state-of-the-art of 55.7% PQ on the COCO test-dev set.

关键词： computer vision Art computer architecture Transformers pattern recognition Task analysis

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Video Frame Interpolation with Transformer

Video Frame Interpolation with Transformer

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

作者： Lu, Liying Wu, Ruizheng Lin, Huaijia Lu, Jiangbo Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years. Existing methods built upon convolutional networks generally face challenges of handling large motion due to the locality of convolution operations. To overcome this limitation, we introduce a novel framework, which takes advantage of Transformer to model long-range pixel correlation among video frames. Further, our network is equipped with a novel cross-scale window-based attention mechanism, where cross-scale windows interact with each other. This design effectively enlarges the receptive field and aggregates multi-scale information. Extensive quantitative and qualitative experiments demonstrate that our method achieves new state-of-the-art results on various benchmarks.

关键词： Interpolation computer vision Correlation Convolution Aggregates Face recognition Computational modeling

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MViTv2: Improved Multiscale vision Transformers for Classification and Detection

MViTv2: Improved Multiscale Vision Transformers for Classifi...

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

作者： Li, Yanghao Wu, Chao-Yuan Fan, Haoqi Mangalam, Karttikeya Xiong, Bo Malik, Jitendra Feichtenhofer, Christoph Facebook AI Res Seattle WA 98101 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we study Multiscale vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 AP(box) on COCO object detection as well as 86.1% on Kinetics-400 video classification. Code and models are available at https: //***/facebookresearch/mvit.

关键词： Representation learning Visualization Image segmentation Image recognition computer architecture Object detection Transformers

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Exploiting Explainable Metrics for Augmented SGD

Exploiting Explainable Metrics for Augmented SGD

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

作者： Hosseini, Mahdi S. Tuli, Mathieu Plataniotis, Konstantinos N. Univ New Brunswick Fredericton NB Canada Univ Toronto Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Explaining the generalization characteristics of deep learning is an emerging topic in advanced machine learning. There are several unanswered questions about how learning under stochastic optimization really works and why certain strategies are better than others. In this paper, we address the following question: can we probe intermediate layers of a deep neural network to identify and quantify the learning quality of each layer? With this question in mind, we propose new explainability metrics that measure the redundant information in a network's layers using a low-rank factorization framework and quantify a complexity measure that is highly correlated with the generalization performance of a given optimizer, network, and dataset. We subsequently exploit these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by adaptively adjusting the learning rate in each layer to improve in generalization performance. Our augmented SGD - dubbed RMSGD - introduces minimal computational overhead compared to SOTA methods and outperforms them by exhibiting strong generalization characteristics across application, architecture, and dataset.

关键词： Measurement Deep learning computer vision Neural networks computer architecture pattern recognition Complexity theory

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Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face recognition

Identity-driven Three-Player Generative Adversarial Network ...

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

作者： Kolf, Jan Niklas Rieber, Tim Elliesen, Jurek Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9798350302493

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available (1).

关键词： Face recognition

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Scaling vision Transformers

Scaling Vision Transformers

引用

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

作者： Zhai, Xiaohua Kolesnikov, Alexander Houlsby, Neil Beyer, Lucas Google Res Brain Team Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Attention-based neural networks such as the vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class.

关键词： Training computer vision Error analysis Computational modeling Neural networks Memory management Training data

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Revisiting Near/Remote Sensing with Geospatial Attention

Revisiting Near/Remote Sensing with Geospatial Attention

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

作者： Workman, Scott Rafique, M. Usman Blanton, Hunter Jacobs, Nathan DZYNE Technol Fairfax VA 22031 USA Kitware Inc Clifton Pk NY USA Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.

关键词： Image segmentation computer vision Social networking (online) Computational modeling computer architecture Geospatial analysis Sensors

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