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SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

SetVAE: Learning Hierarchical Composition for Generative Mod...

引用

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

作者： Kim, Jinwoo Yoo, Jaehoon Lee, Juho Hong, Seunghoon Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision.

关键词： computer vision Art Data models Cognition Encoding pattern recognition Task analysis

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Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes

Towards Rolling Shutter Correction and Deblurring in Dynamic...

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

作者： Zhong, Zhihang Zheng, Yinqiang Sato, Imari Univ Tokyo Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781665445092

Joint rolling shutter correction and deblurring (RSCD) techniques are critical for the prevalent CMOS cameras. However, current approaches are still based on conventional energy optimization and are developed for static scenes. To enable learning-based approaches to address real-world RSCD problem, we contribute the first dataset, BS-RSCD, which includes both ego-motion and object-motion in dynamic scenes. Real distorted and blurry videos with corresponding ground truth are recorded simultaneously via a beam-splitter-based acquisition system. Since direct application of existing individual rolling shutter correction (RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirable results due to inherent flaws in the network architecture, we further present the first learning-based model (JCD) for RSCD. The key idea is that we adopt bi-directional warping streams for displacement compensation, while also preserving the non-warped deblurring stream for details restoration. The experimental results demonstrate that JCD achieves state-of-the-art performance on the realistic RSCD dataset (BS-RSCD) and the synthetic RSC dataset (Fastec-RS).

关键词： Measurement Learning systems computer vision Codes Bidirectional control Network architecture pattern recognition

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A Hyperbolic-to-Hyperbolic Graph Convolutional Network

A Hyperbolic-to-Hyperbolic Graph Convolutional Network

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

作者： Dai, Jindou Wu, Yuwei Gao, Zhi Jia, Yunde Beijing Inst Technol BIT Lab Intelligent Informat Technol Sch Comp Sci Beijing 100081 Peoples R China

ISBN: (纸本)9781665445092

Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is inferior because tangent space is only a local approximation of a manifold. In this paper, we propose a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that directly works on hyperbolic manifolds. Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation. The hyperbolic feature transformation works as linear transformation on hyperbolic manifolds. It ensures the transformed node representations still lie on the hyperbolic manifold by imposing the orthogonal constraint on the transformation sub-matrix. The hyperbolic neighborhood aggregation updates each node representation via the Einstein midpoint. The H2H-GCN avoids the distortion caused by tangent space approximations and keeps the global hyperbolic structure. Extensive experiments show that the H2H-GCN achieves substantial improvements on the link prediction, node classification, and graph classification tasks.

关键词： Manifolds Geometry computer vision Convolution Distortion pattern recognition Task analysis

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Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed recognition

Class-Conditional Sharpness-Aware Minimization for Deep Long...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhipeng Zhou Lanqing Li Peilin Zhao Pheng-Ann Heng Wei Gong University of Science and Technology of China The Chinese University of Hong Kong Zhejiang Lab Tencent AI Lab

It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly imbalanced label distribution. In this paper, through empirical observations, we argue that sharp minima are in fact prevalent in deep long-tailed models, whereas naï ve integration of existing flattening operations into long-tailed learning algorithms brings little improvement. Instead, we propose an effective two-stage sharpness-aware optimization approach based on the decoupling paradigm in DLTR. In the first stage, both the feature extractor and classifier are trained under parameter perturbations at a class-conditioned scale, which is theoretically motivated by the characteristic radius of flat minima under the PAC-Bayesian framework. In the second stage, we generate adversarial features with class-balanced sampling to further robustify the classifier with the backbone frozen. Extensive experiments on multiple long-tailed visual recognition benchmarks show that, our proposed Class-Conditional Sharpness-Aware Minimization (CC-SAM), achieves competitive performance compared to the state-of-the-arts. Code is available at https://***/zzpustc/CC-SAM.

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Representative Forgery Mining for Fake Face Detection

Representative Forgery Mining for Fake Face Detection

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

作者： Wang, Chengrui Deng, Weihong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665445092

Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially, our method is simple-touse and can be easily integrated with various CNN models. Extensive experiments show that the detector trained with our method is capable to separately point out the representative forgery of fake faces generated by different manipulation techniques, and our method enables a vanilla CNN-based detector to achieve state-of-the-art performance without structure modification. Our code is available at https://***/crywang/RFM.

关键词： Codes Face recognition Refining Training data Data visualization Detectors Forgery

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Unbalanced Feature Transport for Exemplar-based Image Translation

Unbalanced Feature Transport for Exemplar-based Image Transl...

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

作者： Zhan, Fangneng Yu, Yingchen Cui, Kaiwen Zhang, Gongjie Lu, Shijian Pan, Jianxiong Zhang, Changgong Ma, Feiying Xie, Xuansong Miao, Chunyan Nanyang Technol Univ Singapore Singapore Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images with reference styles remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation framework that incorporates optimal transport for feature alignment between conditional inputs and style exemplars in image translation. The introduction of optimal transport mitigates the constraint of many-to-one feature matching significantly while building up accurate semantic correspondences between conditional inputs and exemplars. We design a novel unbalanced optimal transport to address the transport between features with deviational distributions which exists widely between conditional inputs and exemplars. In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully. Extensive experiments over multiple image translation tasks show that our method achieves superior image translation qualitatively and quantitatively as compared with the state-of-the-art.

关键词： Image segmentation computer vision Image edge detection Semantics Buildings Coherence pattern recognition

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Troubleshooting Blind Image Quality Models in the Wild

Troubleshooting Blind Image Quality Models in the Wild

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

作者： Wang, Zhihua Wang, Haotao Chen, Tianlong Wang, Zhangyang Ma, Kede City Univ Hong Kong Hong Kong Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.

关键词： Image quality Measurement computer vision Computational modeling Mathematical models pattern recognition Computational efficiency

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Rethinking and Improving the Robustness of Image Style Transfer

Rethinking and Improving the Robustness of Image Style Trans...

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

作者： Wang, Pei Li, Yijun Vasconcelos, Nuno UC La Jolla CA 92093 USA Adobe Res Los Altos CA USA

ISBN: (纸本)9781665445092

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

关键词： Visualization Smoothing methods Correlation computer architecture Network architecture Feature extraction Robustness

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OvarNet: Towards Open-Vocabulary Object Attribute recognition

OvarNet: Towards Open-Vocabulary Object Attribute Recognitio...

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conference on computer vision and pattern recognition (cvpr)

作者： Keyan Chen Xiaolong Jiang Yao Hu Xu Tang Yan Gao Jianqi Chen Weidi Xie Beihang University Xiaohongshu Inc CMIC Shanghai Jiao Tong University Shanghai AI Laboratory

In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a naive two-stage approach for open-vocabulary object detection and attribute classification, termed CLIP-Attr. The candidate objects are first proposed with an offline RPN and later classified for semantic category and attributes; (ii) we combine all available datasets and train with a federated strategy to finetune the CLIP model, aligning the visual representation with attributes, additionally, we investigate the efficacy of leveraging freely available online image-caption pairs under weakly supervised learning; (iii) in pursuit of efficiency, we train a Faster-RCNN type model end-to-end with knowledge distillation, that performs class-agnostic object proposals and classification on semantic categories and attributes with classifiers generated from a text encoder; Finally, (iv) we conduct extensive experiments on VAW, MS-COCO, LSA, and OVAD datasets, and show that recognition of semantic category and attributes is complementary for visual scene understanding, i.e., jointly training object detection and attributes prediction largely outperform existing approaches that treat the two tasks independently, demonstrating strong generalization ability to novel attributes and categories.

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Real-Time High-Resolution Background Matting

Real-Time High-Resolution Background Matting

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

作者： Lin, Shanchuan Ryabtsev, Andrey Sengupta, Soumyadip Curless, Brian Seitz, Steve Kemelmacher-Shlizerman, Ira Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. Our technique is based on background matting, where an additional frame of the background is captured and used in recovering the alpha matte and the foreground layer. The main challenge is to compute a high-quality alpha matte, preserving strand-level hair details, while processing high-resolution images in real-time. To achieve this goal, we employ two neural networks;a base network computes a low-resolution result which is refined by a second network operating at high-resolution on selective patches. We introduce two large-scale video and image matting datasets: VideoMatte240K and PhotoMatte 13K/85. Our approach yields higher quality results compared to the previous state-of-the-art in background matting, while simultaneously yielding a dramatic boost in both speed and resolution.

关键词： Hair Privacy Image resolution Image recognition Neural networks Graphics processing units Watermarking

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