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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是81-90 订阅

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Transformer Interpretability Beyond Attention Visualization

Transformer Interpretability Beyond Attention Visualization

引用

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

作者： Chefer, Hila Gur, Shir Wolf, Lior Tel Aviv Univ Sch Comp Sci Tel Aviv Israel Facebook AI Res FAIR Tel Aviv Israel

ISBN: (纸本)9781665445092

Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods.

关键词： Visualization computer vision Head Text categorization Neural networks Transformers pattern recognition

来源：评论

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StylePeople: A Generative Model of Fullbody Human Avatars

StylePeople: A Generative Model of Fullbody Human Avatars

引用

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

作者： Grigorev, Artur Iskakov, Karim Ianina, Anastasia Bashirov, Renat Zakharkin, Ilya Vakhitov, Alexander Lempitsky, Victor Samsung AI Ctr Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665445092

We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches. We also show how these avatars can be created from multiple frames of a video using backpropagation. We then propose a generative model for such avatars that can be trained from datasets of images and videos of people. The generative model allows us to sample random avatars as well as to create dressed avatars of people from one or few images. The code for the project is available at ***/style-people.

关键词： Hair Geometry computer vision Avatars Computational modeling Clothing Video sequences

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Part-aware Panoptic Segmentation

Part-aware Panoptic Segmentation

引用

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

作者： de Geus, Daan Meletis, Panagiotis Lu, Chenyang Wen, Xiaoxiao Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665445092

In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.

关键词： Measurement Training Technological innovation computer vision Codes Annotations Merging

来源：评论

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DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

DetectoRS: Detecting Objects with Recursive Feature Pyramid ...

引用

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

作者： Qiao, Siyuan Chen, Liang-Chieh Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available(1).

关键词： computer vision Philosophical considerations Codes Convolution Detectors Switches Object detection

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Convolutional Neural Network Pruning with Structural Redundancy Reduction

Convolutional Neural Network Pruning with Structural Redunda...

引用

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

作者： Wang, Zi Li, Chengcheng Wang, Xiangyang Univ Tennessee Knoxville TN 37996 USA Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Convolutional neural network (CNN) pruning has become one of the most successful network compression approaches in recent years. Existing works on network pruning usually focus on removing the least important filters in the network to achieve compact architectures. In this study, we claim that identifying structural redundancy plays a more essential role than finding unimportant filters, theoretically and empirically. We first statistically model the network pruning problem in a redundancy reduction perspective and find that pruning in the layer(s) with the most structural redundancy outperforms pruning the least important filters across all layers. Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy. Experiments on various benchmark network architectures and datasets show that our proposed approach significantly outperforms the previous state-of-the-art.

关键词： computer vision Image synthesis Redundancy Object detection computer architecture Network architecture pattern recognition

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Limitations of Post-Hoc Feature Alignment for Robustness

Limitations of Post-Hoc Feature Alignment for Robustness

引用

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

作者： Burns, Collin Steinhardt, Jacob Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781665445092

Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the two distributions in a trained neural network. This technique has received renewed interest lately because of its impressive performance on robustness benchmarks. However, when and why this method works is not well understood. We investigate the approach in more detail and identify several limitations. We show that it only significantly helps with a narrow set of distribution shifts and we identify several settings in which it even degrades performance. We also explain why these limitations arise by pinpointing why this approach can be so effective in the first place. Our findings call into question the utility of this approach and Unsupervised Domain Adaptation more broadly for improving robustness in practice.

关键词： Training Knowledge engineering computer vision Neural networks Buildings Benchmark testing Robustness

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Explicit Knowledge Incorporation for Visual Reasoning

Explicit Knowledge Incorporation for Visual Reasoning

引用

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

作者： Zhang, Yifeng Jiang, Ming Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665445092

Existing explainable and explicit visual reasoning methods only perform reasoning based on visual evidence but do not take into account knowledge beyond what is in the visual scene. To addresses the knowledge gap between visual reasoning methods and the semantic complexity of real-world images, we present the first explicit visual reasoning method that incorporates external knowledge and models high-order relational attention for improved generalizability and explainability. Specifically, we propose a knowledge incorporation network that explicitly creates and includes new graph nodes for entities and predicates from external knowledge bases to enrich the semantics of the scene graph used in explicit reasoning. We then create a novel Graph-Relate module to perform high-order relational attention on the enriched scene graph. By explicitly introducing structured external knowledge and high-order relational attention, our method demonstrates significant generalizability and explainability over the state-of-the-art visual reasoning approaches on the GQA and VQAv2 datasets.

关键词： Knowledge engineering Visualization computer vision Semantics Knowledge based systems Cognition pattern recognition

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Surrogate Gradient Field for Latent Space Manipulation

Surrogate Gradient Field for Latent Space Manipulation

引用

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

作者： Li, Minjun Jin, Yanghua Zhu, Huachun Preferred Networks Tokyo Japan

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its underlying latent code, but rarely go beyond the basic task of attribute adjustment. We propose the first method that enables manipulation with multidimensional condition such as keypoints and captions. Specifically, we design an algorithm that searches for a new latent code that satisfies the target condition based on the Surrogate Gradient Field (SGF) induced by an auxiliary mapping network. For quantitative comparison, we propose a metric to evaluate the disentanglement of manipulation methods. Thorough experimental analysis on the facial attribute adjustment task shows that our method outperforms state-of-the-art methods in disentanglement. We further apply our method to tasks of various condition modalities to demonstrate that our method can alter complex image properties such as keypoints and captions.

关键词： Measurement computer vision Codes Natural languages Aerospace electronics Generative adversarial networks pattern recognition

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Hallucination Improves Few-Shot Object Detection

Hallucination Improves Few-Shot Object Detection

引用

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

作者： Zhang, Weilin Wang, Yu-Xiong Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781665445092

Learning to detect novel objects from few annotated examples is of great practical importance. A particularly challenging yet common regime occurs when there are extremely limited examples (less than three). One critical factor in improving few-shot detection is to address the lack of variation in training data. We propose to build a better model of variation for novel classes by transferring the shared within-class variation from base classes. To this end, we introduce a hallucinator network that learns to generate additional, useful training examples in the region of interest (RoI) feature space, and incorporate it into a modern object detection model. Our approach yields significant performance improvements on two state-of-the-art few-shot detectors with different proposal generation procedures. In particular, we achieve new state of the art in the extremely-few-shot regime on the challenging COCO benchmark.

关键词： Training computer vision Computational modeling Training data Object detection Detectors Benchmark testing

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Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses

Roof-GAN: Learning to Generate Roof Geometry and Relations f...

引用

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

作者： Qian, Yiming Zhang, Hao Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. Given the number of primitives, the generator produces a structured roof model as a graph, which consists of 1) primitive geometry as raster images at each node, encoding facet segmentation and angles;2) inter-primitive colinear/coplanar relationships at each edge;and 3) primitive geometry in a vector format at each node, generated by a novel differentiable vectorizer while enforcing the relationships. The discriminator is trained to assess the primitive raster geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture. Qualitative and quantitative evaluations demonstrate the effectiveness of our approach in generating diverse and realistic roof models over the competing methods with a novel metric proposed in this paper for the task of structured geometry generation.

关键词： Geometry Measurement Image segmentation computer vision Image coding Image edge detection Generative adversarial networks

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