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SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing

SemanticStyleGAN: Learning Compositional Generative Priors f...

引用

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

作者： Shi, Yichun Yang, Xiao Wan, Yangyue Shen, Xiaohui ByteDance Inc Culver City CA 90230 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are designed to control global styles, it is hard to achieve a fine-grained control over synthesized images. We present SemanticStyleGAN, where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way. The structure and texture of different local parts are controlled by corresponding latent codes. Experimental results demonstrate that our model provides a strong disentanglement between different spatial areas. When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images. The model can also be extended to other domains via transfer learning. Thus, as a generic prior model with built-in disentanglement, it could facilitate the development of GAN-based applications and enable more potential downstream tasks.

关键词： Deep learning Adaptation models Image segmentation Codes Image synthesis Face recognition Semantics

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Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

Pastiche Master: Exemplar-Based High-Resolution Portrait Sty...

引用

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

作者： Yang, Shuai Jiang, Liming Liu, Ziwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control. Code is available at https://***/williamyang1991/DualStyleGAN.

关键词： computer vision Codes Image color analysis Face recognition Computational modeling Transfer learning Transforms

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CMT: Convolutional Neural Networks Meet vision Transformers

CMT: Convolutional Neural Networks Meet Vision Transformers

引用

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

作者： Guo, Jianyuan Han, Kai Wu, Han Tang, Yehui Chen, Xinghao Wang, Yunhe Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision transformers have been successfully applied to image recognition tasks due to their ability to capture longrange dependencies within an image. However, there are still gaps in both performance and computational cost between transformers and existing convolutional neural networks (CNNs). In this paper, we aim to address this issue and develop a network that can outperform not only the canonical transformers, but also the high-performance convolutional models. We propose a new transformer based hybrid network by taking advantage of transformers to capture long-range dependencies, and of CNNs to extract local information. Furthermore, we scale it to obtain a family of models, called CMTs, obtaining much better trade-off for accuracy and efficiency than previous CNN-based and transformer-based models. In particular, our CMT-S achieves 83.5% top-1 accuracy on ImageNet, while being 14x and 2x smaller on FLOPs than the existing DeiT and EfficientNet, respectively. The proposed CMT-S also generalizes well on CIFAR10 (99.2%), CIFAR100 (91.7%), Flowers (98.7%), and other challenging vision datasets such as COCO (44.3% mAP), with considerably less computational cost.

关键词： computer vision Visualization Image recognition Force computer architecture Object detection Transformers

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Attentive Fine-Grained Structured Sparsity for Image Restoration

Attentive Fine-Grained Structured Sparsity for Image Restora...

引用

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

作者： Oh, Junghun Kim, Heewon Nah, Seungjun Hong, Cheeun Choi, Jonghyun Lee, Kyoung Mu ASRI Dept ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea NVIDIA Santa Clara CA USA Yonsei Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models. Despite the outstanding performance, the heavy computation demanded by the deep models has restricted the application of image restoration. To lift the restriction, it is required to reduce the size of the networks while maintaining accuracy. Recently, N:M structured pruning has appeared as one of the effective and practical pruning approaches for making the model efficient with the accuracy constraint. However, it fails to account for different computational complexities and performance requirements for different layers of an image restoration network. To further optimize the trade-off between the efficiency and the restoration accuracy, we propose a novel pruning method that determines the pruning ratio for N: M structured sparsity at each layer. Extensive experimental results on super-resolution and deblurring tasks demonstrate the efficacy of our method which outperforms previous pruning methods significantly PyTorch implementation for the proposed methods will be publicly available at https://***/JungHunOh/SLS_cvpr2022

关键词： Photography computer vision Computational modeling Superresolution Image restoration Computational efficiency pattern recognition

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Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

Sparse Local Patch Transformer for Robust Face Alignment and...

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

作者： Xia, Jiahao Qu, Weiwei Huang, Wenjian Zhang, Jianguo Wang, Xi Xu, Min Univ Technol Sydney Fac Engn & IT Ultimo Australia Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China CaimCar Troy NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website(1).

关键词： Heating systems computer vision Codes Face recognition Aggregates Benchmark testing Transformers

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Sparse Instance Activation for Real-Time Instance Segmentation

Sparse Instance Activation for Real-Time Instance Segmentati...

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

作者： Cheng, Tianheng Wang, Xinggang Chen, Shaoyu Zhang, Wenqiang Zhang, Qian Huang, Chang Zhang, Zhaoxiang Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Horizon Robot Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to highlight informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, Sparselnst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly outperforms the counterparts in terms of speed and accuracy. Code and models are available at https : //github com/hustvl/SparseInst.

关键词： Convolutional codes computer vision Aggregates Object detection Benchmark testing Real-time systems pattern recognition

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TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed

TO-FLOW: Efficient Continuous Normalizing Flows with Tempora...

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

作者： Du, Shian Luo, Yihong Chen, Wei Xu, Jian Zeng, Delu South China Univ Technol Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on large datasets due to the incremental complexity of the neural ODE training. Optimal Transport theory has been applied to regularize the dynamics of the ODE to speed up training in recent works. In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training. In this appoach, we optimize the network weights of the CNF alternately with evolutionary time by coordinate descent. Further with temporal regularization, stability of the evolution is ensured. This approach can be used in conjunction with the original regularization approach. We have experimentally demonstrated that the proposed approach can significantly accelerate training without sacrifying performance over baseline models.

关键词： Training computer vision Statistical analysis Computational modeling computer architecture Ordinary differential equations Stability analysis

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Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities

Assembly101: A Large-Scale Multi-View Video Dataset for Unde...

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

作者： Sener, Fadime Chatterjee, Dibyadip Shelepov, Daniel He, Kun Singhania, Dipika Wang, Robert Yao, Angela Meta Real Labs Res Pittsburgh PA 15222 USA Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and IM fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems.

关键词： Training computer vision Three-dimensional displays Toy manufacturing industry Recording pattern recognition Synchronization

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Interactive Multi-Class Tiny-Object Detection

Interactive Multi-Class Tiny-Object Detection

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

作者： Lee, Chunggi Park, Seonwook Song, Heon Ryu, Jeongun Kim, Sanghoon Kim, Haejoon Pereira, Sergio Yoo, Donggeun Lunit Inc Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of computer vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interactive annotation setting for the detection task has thus far been unexplored. To address these needs, we propose a novel interactive annotation method for multiple instances of tiny objects from multiple classes, based on a few point-based user inputs. Our approach, C3Det, relates the full image context with annotator inputs in a local and global manner via late-fusion and feature-correlation, respectively. We perform experiments on the Tiny-DOTA and LCell datasets using both two-stage and one-stage object detection architectures to verify the efficacy of our approach. Our approach outperforms existing approaches in interactive annotation, achieving higher mAP with fewer clicks. Furthermore, we validate the annotation efficiency of our approach in a user study where it is shown to be 2.85x faster and yield only 0.36x task load (NASA-TI X, lower is better) compared to manual annotation. The code is available at https : //***/ChungYi347/Interactive -Multi-Class-Tiny-Object-Detection.

关键词： computer vision Costs Annotations Machine vision Training data Object detection computer architecture

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MeMViT: Memory-Augmented Multiscale vision Transformer for Efficient Long-Term Video recognition

MeMViT: Memory-Augmented Multiscale Vision Transformer for E...

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

作者： Wu, Chao-Yuan Li, Yanghao Mangalam, Karttikeya Fan, Haoqi Xiong, Bo Malik, Jitendra Feichtenhofer, Christoph Facebook AI Res Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale vision Transformer, that has a temporal support 30xlonger than existing models with only 4.5% more compute;traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models will be made publicly available.

关键词： computer vision Visualization Costs Codes Computational modeling Cache memory computer architecture

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