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TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

TediGAN: Text-Guided Diverse Face Image Generation and Manip...

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

作者： Xia, Weihao Yang, Yujiu Xue, Jing-Hao Wu, Baoyuan Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China UCL Dept Stat Sci London England Chinese Univ Hongkong Sch Data Sci Shenzhen Peoples R China Shenzhen Res Inst Big Data Secure Comp Lab Big Data Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 1024(2). Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method.

关键词： Image segmentation computer vision Image resolution Image synthesis Face recognition Semantics Aerospace electronics

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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning 30

A Gift from Knowledge Distillation: Fast Optimization, Netwo...

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30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yim, Junho Joo, Donggyu Bae, Jihoon Kim, Junmo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781538604571

We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN maps from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model;(2) the student DNN outperforms the original DNN;and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.

关键词： Training Optimization Knowledge engineering Feature extraction Knowledge transfer computer vision Transfer (Psychology) Knowledge based engineering images computer vision Feature extraction Students TWO-LAYER Functional training Epistemology Training

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EDTER: Edge Detection with Transformer

EDTER: Edge Detection with Transformer

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

作者： Pu, Mengyang Huang, Yaping Liu, Yuming Guan, Qingji Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually suppressed with the enlarging of receptive fields. Recently, vision transformer has shown excellent capability in capturing long-range dependencies. Inspired by this, we propose a novel transformer-based edge detector, Edge Detection TransformER (EDTLR), to extract clear and crisp object boundaries and meaningful edges by exploiting the full image context information and detailed local cues simultaneously. EDTER works in two stages. In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches. Then in Stage II, a local transformer encoder works on fine-grained patches to excavate the short-range local cues. Each transformer encoder is followed by an elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve high-resolution features. Finally, the global context and local cues are combined by a Feature Fusion Module and fed into a decision head for edge prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate the superiority of EDTER in comparison with state-of-the-arts.

关键词： computer vision Head Image edge detection Semantics Detectors Transformers Feature extraction

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A Biresolution Spectral framework for Product Quantization 31

A Biresolution Spectral framework for Product Quantization

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Mukherjee, Lopamudra Ravi, Sathya N. Peng, Jiming Singh, Vikas Univ Wisconsin Whitewater WI 53190 USA Univ Wisconsin Madison WI USA Univ Houston Houston TX 77004 USA

ISBN: (纸本)9781538664209

Product quantization (PQ) (and its variants) has been effectively used to encode high-dimensional data into compact codes for many problems in vision. In principle, PQ decomposes the given data into a number of lower-dimensional subspaces where the quantization proceeds independently for each subspace. While the original PQ approach does not explicitly optimize for these subspaces, later proposals have argued that the performance tends to benefit significantly if such subspaces are chosen in an optimal manner. Despite such consensus, existing approaches in the literature diverge in terms of which specific properties of these subspaces are desirable and how one should proceed to solve/optimize them. Nonetheless, despite the empirical support, there is less clarity regarding the theoretical properties that underlie these experimental benefits for quantization problems in general. In this paper, we study the quantization problem in the setting where subspaces are orthogonal and show that this problem is intricately related to a specific type of spectral decomposition of the data. This insight not only opens the door to a rich body of work in spectral analysis, but also leads to distinct computational benefits. Our resultant biresolution spectral formulation captures both the subspace projection error as well as the quantization error within the same framework. After a reformulation, the core steps of our algorithm involve a simple eigen decomposition step, which can be solved efficiently. We show that our method performs very favorably against a number of state of the art methods on standard data sets.

关键词： Quantization (signal) Additives computer vision Optimization Matrix decomposition Proposals Indexing

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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

GAPartNet: Cross-Category Domain-Generalizable Object Percep...

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

作者： Geng, Haoran Xu, Helin Zhao, Chengyang Xu, Chao Yi, Li Huang, Siyuan Wang, He Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350301298

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world.

关键词： Embodied vision: Active agents simulation

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Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More 32

Student Becoming the Master: Knowledge Amalgamation for Join...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ye, Jingwen Ji, Yixin Wang, Xinchao Ou, Kairi Tao, Dapeng Song, Mingli Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Stevens Inst Technol Dept Comp Sci Hoboken NJ USA Alibaba Grp Hangzhou Peoples R China Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9781728132938

In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates the knowledge and masters the expertise of two pre-trained teacher models working on heterogeneous problems, one on scene parsing and the other on depth estimation. To this end, we propose an innovative training strategy that learns the parameters of the student intertwined with the teachers, achieved by "projecting'' its amalgamated features onto each teacher's domain and computing the loss. We also introduce two options to generalize the proposed training strategy to handle three or more tasks simultaneously. The proposed scheme yields very encouraging results. As demonstrated on several benchmarks, the trained student model achieves results even superior to those of the teachers in their own expertise domains and on par with the state-of-the-art fully supervised models relying on human-labelled annotations.

关键词： Deep Learning vision Applications and Systems

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The 6th AI City Challenge

The 6th AI City Challenge

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

作者： Naphade, Milind Wang, Shuo Anastasiu, David C. Tang, Zheng Chang, Ming-Ching Yao, Yue Zheng, Liang Rahman, Mohammed Shaiqur Venkatachalapathy, Archana Sharma, Anuj Feng, Qi Ablavsky, Vitaly Sclaroff, Stan Chakraborty, Pranamesh Li, Alice Li, Shangru Chellappa, Rama NVIDIA Corp Santa Clara CA 95051 USA Santa Clara Univ Santa Clara CA 95053 USA SUNY Albany Albany NY 12222 USA Australian Natl Univ Canberra ACT Australia Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Iowa State Univ Ames IA USA Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.

关键词： computer vision Mortar Urban areas Focusing Cameras Safety pattern recognition

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Generative Diffusion Prior for Unified Image Restoration and Enhancement

Generative Diffusion Prior for Unified Image Restoration and...

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

作者： Fei, Ben Lyu, Zhaoyang Pan, Liang Zhang, Junzhe Yang, Weidong Luo, Tianyue Zhang, Bo Dai, Bo Fudan Univ Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Existing image restoration methods mostly leverage the posterior distribution of natural images. However, they often assume known degradation and also require supervised training, which restricts their adaptation to complex real applications. In this work, we propose the Generative Diffusion Prior (GDP) to effectively model the posterior distributions in an unsupervised sampling manner. GDP utilizes a pre-train denoising diffusion generative model (DDPM) for solving linear inverse, non-linear, or blind problems. Specifically, GDP systematically explores a protocol of conditional guidance, which is verified more practical than the commonly used guidance way. Furthermore, GDP is strength at optimizing the parameters of degradation model during the denoising process, achieving blind image restoration. Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions. Experimentally, we demonstrate GDP 's versatility on several image datasets for linear problems, such as super-resolution, deblurring, inpainting, and colorization, as well as non-linear and blind issues, such as low-light enhancement and HDR image recovery. GDP outperforms the current leading unsupervised methods on the diverse benchmarks in reconstruction quality and perceptual quality. Moreover, GDP also generalizes well for natural images or synthesized images with arbitrary sizes from various tasks out of the distribution of the ImageNet training set. The project page is available at https://***/

关键词： Low-level vision

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SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval 31

SketchMate: Deep Hashing for Million-Scale Human Sketch Retr...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Peng Huang, Yongye Yuan, Tongtong Pang, Kaiyue Song, Yi-Zhe Xiang, Tao Hospedales, Timothy M. Ma, Zhanyu Guo, Jun Beijing Univ Posts & Telecommun Beijing Peoples R China Queen Mary Univ London SketchX London England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781538664209

We propose a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset. Leveraging on this large dataset, we explore a few sketch-specific traits that were otherwise under-studied in prior literature. Instead of following the conventional sketch recognition task, we introduce the novel problem of sketch hashing retrieval which is not only more challenging, but also offers a better testbed for large-scale sketch analysis, since: (i) more fine-grained sketch feature learning is required to accommodate the large variations in style and abstraction, and (ii) a compact binary code needs to be learned at the same time to enable efficient retrieval. Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN RN N architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches. By working with a 3.8M sketch dataset, we show that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data. Our network on the other hand not only offers the best retrieval performance on various code sizes, but also yields the best generalization performance under a zero-shot setting and when re-purposed for sketch recognition. Such superior performances effectively demonstrate the benefit of our sketch-specific design.

关键词： Task analysis Visualization Semantics Binary codes Quantization (signal) Entropy computer architecture

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3DeformRS: Certifying Spatial Deformations on Point Clouds

3DeformRS: Certifying Spatial Deformations on Point Clouds

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

作者： Perez S, Gabriel Perez, Juan C. Alfarra, Motasem Giancola, Silvio Ghanem, Bernard Univ Nacl Colombia Bogota Colombia King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia KAUST Thuwal Saudi Arabia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these models against real-world deformations must be addressed practically and reliably. In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations. We developed 3DeformRS by building upon recent work that generalized Randomized Smoothing (RS) from pixel-intensity perturbations to vector-field deformations. In particular, we specialized RS to certify DNNs against parameterized deformations (e.g. rotation, twisting), while enjoying practical computational costs. We leverage the virtues of 3DeformRS to conduct a comprehensive empirical study on the certified robustness of four representative point cloud DNNs on two datasets and against seven different deformations. Compared to previous approaches for certifying point cloud DNNs, 3DeformRS is fast, scales well with point cloud size, and provides comparable-to-better certificates. For instance, when certifying a plain PointNet against a 3 degrees z-rotation on 1024-point clouds, 3DeformRS grants a certificate 3x larger and 20x faster than previous work(1).

关键词： Point cloud compression computer vision Solid modeling Three-dimensional displays Smoothing methods Computational modeling Perturbation methods

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