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TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing

TransEditor: Transformer-Based Dual-Space GAN for Highly Con...

引用

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

作者： Xu, Yanbo Yin, Yueqin Jiang, Liming Wu, Qianyi Zheng, Chengyao Loy, Chen Change Dai, Bo Wu, Wayne Shanghai AI Lab Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore SenseTime Res Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Monash Univ Melbourne Vic Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing. Code and models are publicly available at https://***/BillyXYB/TransEditor.

关键词： Image quality computer vision Codes Computational modeling Aerospace electronics Transformers Controllability

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An Improved Attention for Visual Question Answering

An Improved Attention for Visual Question Answering

引用

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

作者： Rahman, Tanzila Chou, Shih-Han Sigal, Leonid Carenini, Giuseppe Univ British Columbia Dept Comp Sci Vancouver BC Canada Vector Inst AI Toronto ON Canada Canada CIFAR AI Chair Toronto ON Canada

ISBN: (纸本)9781665448994

We consider the problem of Visual Question Answering (VQA). Given an image and a free-form, open-ended, question, expressed in natural language, the goal of VQA system is to provide accurate answer to this question with respect to the image. The task is challenging because it requires simultaneous and intricate understanding of both visual and textual information. Attention, which captures intra- and inter-modal dependencies, has emerged as perhaps the most widely used mechanism for addressing these challenges. In this paper, we propose an improved attention-based architecture to solve VQA. We incorporate an Attention on Attention (AoA) module within encoder-decoder framework, which is able to determine the relation between attention results and queries. Attention module generates weighted average for each query. On the other hand, AoA module first generates an information vector and an attention gate using attention results and current context;and then adds another attention to generate final attended information by multiplying the two. We also propose multimodal fusion module to combine both visual and textual information. The goal of this fusion module is to dynamically decide how much information should be considered from each modality. Extensive experiments on VQA-v2 benchmark dataset show that our method achieves better performance than the baseline method.

关键词： Visualization computer vision conferences Computational modeling Natural languages computer architecture Logic gates

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MVDiff: Scalable and Flexible Multi-view Diffusion for 3D Object Reconstruction from Single-View

MVDiff: Scalable and Flexible Multi-view Diffusion for 3D Ob...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Emmanuelle Bourigault Pauline Bourigault Department of Engineering Science Visual Geometry Group University of Oxford Department of Computing Imperial College London

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating consistent multiple views for 3D reconstruction tasks is still a challenge to existing image-to-3D diffusion models. Generally, incorporating 3D representations into diffusion model decrease the model’s speed as well as generalizability and quality. This paper proposes a general framework to generate consistent multi-view images from single image or leveraging scene representation transformer and view-conditioned diffusion model. In the model, we introduce epipolar geometry constraints and multi-view attention to enforce 3D consistency. From as few as one image input, our model is able to generate 3D meshes surpassing baselines methods in evaluation metrics, including PSNR, SSIM and LPIPS.

关键词： Geometry Measurement Solid modeling computer vision Three-dimensional displays conferences Diffusion models

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Local Attention Pyramid for Scene Image Generation

Local Attention Pyramid for Scene Image Generation

引用

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

作者： Shim, Sang-Heon Hyun, Sangeek Bae, DaeHyun Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.

关键词： Measurement Deep learning Visualization Image segmentation computer vision Image analysis Image synthesis

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Christoph Reich Biplob Debnath Deep Patel Tim Prangemeier Daniel Cremers Srimat Chakradhar NEC Laboratories America Inc. TU Munich TU Darmstadt Munich Center for Machine Learning (MCML)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Video coding Computational modeling Face recognition Pipelines Bandwidth Self-supervised learning Distortion

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N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation

N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain A...

引用

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

作者： Cannici, Marco Plizzari, Chiara Planamente, Mirco Ciccone, Marco Bottino, Andrea Caputo, Barbara Matteucci, Matteo Politecn Milan Milan Italy Politecn Torino Turin Italy Ist Italiano Tecnol Genoa Italy

ISBN: (纸本)9781665448994

Event cameras are novel neuromorphic sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Event simulation from existing RGB datasets is commonly used to overcome the need of large amount of annotated data, which lacks due to the novelty of the event sensors. In this context, the possibility of using event simulation in synthetic scenarios, where data generation is not limited to pre-existing datasets, is to date still unexplored. In this work, we analyze the synth-to-real domain shift in event data, i.e., the gap arising between simulated events obtained from synthetic renderings and those captured with a real camera on real images. To this purpose, we extend to the event modality the popular RGB-D Object Dataset (ROD), which already comes with its synthetic version (SynROD). The resulting Neuromorphic ROD dataset (N-ROD) is the first to enable a synth-to-real analysis on event data, showing the effectiveness of Domain Adaptation techniques in reducing the synth-to-real shift. Moreover, through extensive experiments on multi-modal RGBE data, we show that events can be effectively combined with conventional visual information, encouraging further research in this area. The N-ROD dataset is available at https://***/home/.

关键词： Visualization computer vision Neuromorphics conferences Cameras Rendering (computer graphics) Data models

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Learning Optical Flow with Kernel Patch Attention

Learning Optical Flow with Kernel Patch Attention

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

作者： Luo, Ao Yang, Fan Li, Xin Liu, Shuaicheng Megvii Technol Beijing Peoples R China Grp 42 Abu Dhabi U Arab Emirates Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optical flow is a fundamental method used for quantitative motion estimation on the image plane. In the deep learning era, most works treat it as a task of 'matching of features', learning to pull matched pixels as close as possible in feature space and vice versa. However, spatial affinity (smoothness constraint), another important component for motion understanding, has been largely overlooked. In this paper, we introduce a novel approach, called kernel patch attention (KPA), to better resolve the ambiguity in dense matching by explicitly taking the local context relations into consideration. Our KPA operates on each local patch, and learns to mine the context affinities for better inferring the flow fields. It can be plugged into contemporary optical flow architecture and empower the model to conduct comprehensive motion analysis with both feature similarities and spatial relations. On Sintel dataset, the proposed KPA-Flow achieves the best performance with EPE of 1.35 on clean pass and 2.36 on final pass, and it sets a new record of 4.60% in F1-all on KITTI-15 benchmark. Code is publicly available at https://***/megvii-research/KPAFlow.

关键词： computer vision Image motion analysis Motion estimation Benchmark testing pattern recognition Kernel Task analysis

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Meta Agent Teaming Active Learning for Pose Estimation

Meta Agent Teaming Active Learning for Pose Estimation

引用

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

作者： Gong, Jia Fan, Zhipeng Ke, Qiuhong Rahmani, Hossein Liu, Jun Singapore Univ Technol & Design Singapore Singapore NYU New York NY 10003 USA Univ Melbourne Parkville Vic Australia Univ Lancaster Lancaster England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator based on the reward. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. Moreover, to obtain similar pose estimation accuracy, our MATAL framework can save around 40% labeling efforts on average compared to state-of-the-art active learning frameworks.

关键词： computer vision Annotations Computational modeling Accelerated aging Pose estimation Markov processes Benchmark testing

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FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of Dynamic Scenes

FlowIBR: Leveraging Pre-Training for Efficient Neural Image-...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcel Büsching Josef Bengtson David Nilsson Mårten Björkman KTH Royal Insitute of Technology Chalmers University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce FlowIBR, a novel approach for efficient monocular novel view synthesis of dynamic scenes. Existing techniques already show impressive rendering quality but tend to focus on optimization within a single scene without leveraging prior knowledge, resulting in long optimization times per scene. FlowIBR circumvents this limitation by integrating a neural image-based rendering method, pretrained on a large corpus of widely available static scenes, with a per-scene optimized scene flow field. Utilizing this flow field, we bend the camera rays to counteract the scene dynamics, thereby presenting the dynamic scene as if it were static to the rendering network. The proposed method reduces per-scene optimization time by an order of magnitude, achieving comparable rendering quality to existing methods — all on a single consumer-grade GPU.

关键词： computer vision conferences Graphics processing units Rendering (computer graphics) Cameras pattern recognition Optimization

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Spacecraft Time-Series Anomaly Detection Using Transfer Learning

Spacecraft Time-Series Anomaly Detection Using Transfer Lear...

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

作者： Baireddy, Sriram Desai, Sundip R. Mathieson, James L. Foster, Richard H. Chan, Moses W. Comer, Mary L. Delp, Edward J. Purdue Univ Video & Image Proc Lab VIPER W Lafayette IN 47907 USA Adv Technol Ctr Lockheed Martin Space Sunnyvale CA USA

ISBN: (纸本)9781665448994

Anomaly detection in telemetry channels is a high priority for spacecraft, especially when considering the harsh environment of space and the magnitude of launch and operation costs. Traditional spacecraft anomaly detection methods are limited in scope and rely on domain experts to correctly determine abnormal behavior. However, with thousands of distinct telemetry channels being transmitted, the amount of data is difficult to monitor manually. Deep learning models can be used to learn the normal behavior of the telemetry channels and flag or label any deviations. The problem is that we have to train a unique model for each channel to ensure best performance. With the large number of channels to monitor, this may not always be possible. We propose using principles of transfer learning to quickly adapt a general pre-trained model to any specific telemetry channel, greatly reducing the number of unique models needed and reducing the training time for each model. We present the results of our approach on the NASA SMAP/MSL dataset to show that we can achieve performance comparable to state-of-the-art anomaly detection methods.

关键词： Space vehicles Training Adaptation models Transfer learning Predictive models Data models pattern recognition

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