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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Assistive Signals for Deep Neural Network Classifiers

Assistive Signals for Deep Neural Network Classifiers

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pestana, Camilo Liu, Wei Glance, David Owens, Robyn Mian, Ajmal Univ Western Australia 35 Stirling Hwy Crawley WA 6009 Australia

ISBN: (纸本)9781665448994

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model's confidence score regardless if it's under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. 'Assistive Signals' also illustrate bias of ML models towards certain patterns in real-life objects.

关键词： Deep learning computer vision Three-dimensional displays Perturbation methods conferences Optimization methods Lighting

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InstructDiffusion: A Generalist Modeling Interface for vision Tasks

InstructDiffusion: A Generalist Modeling Interface for Visio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zigang Geng Binxin Yang Tiankai Hang Chen Li Shuyang Gu Ting Zhang Jianmin Bao Zheng Zhang Houqiang Li Han Hu Dong Chen Baining Guo University of Science and Technology of China Microsoft Research Asia Southeast University Xi'an Jiaotong University Beijing Normal University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present InstructDiffusion, a unified and generic framework for aligning computer vision tasks with hu-man instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating pro-cess whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint de-tection) and generative tasks (such as editing and enhance-ment) and outperforms prior methods on novel datasets. This represents a solid step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.

关键词： Training computer vision Solid modeling Computational modeling Artificial general intelligence Shoulder Predictive models

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Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

Objectron: A Large Scale Dataset of Object-Centric Videos in...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ahmadyan, Adel Zhang, Liangkai Ablavatski, Artsiom Wei, Jianing Grundmann, Matthias Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14, 819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection and novel view synthesis tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at ***/google-research-datasets/Objectron.

关键词： Measurement Solid modeling Three-dimensional displays Annotations Shape Object detection pattern recognition

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Deep Lucas-Kanade Homography for Multimodal Image Alignment

Deep Lucas-Kanade Homography for Multimodal Image Alignment

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Yiming Huang, Xinming Zhang, Ziming Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA

ISBN: (纸本)9781665445092

Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision applications. In contrast to others, we propose a generic solution to pixel-wise align multimodal image pairs by extending the traditional Lucas-Kanade algorithm with networks. The key contribution in our method is how we construct feature maps, named as deep Lucas-Kanade feature map (DLKFM). The learned DLKFM can spontaneously recognize invariant features under various appearance-changing conditions. It also has two nice properties for the Lucas-Kanade algorithm: (1) The template feature map keeps brightness consistency with the input feature map, thus the color difference is very small while they are well-aligned. (2) The Lucas-Kanade objective function built on DLKFM has a smooth landscape around ground truth homography parameters, so the iterative solution of the Lucas-Kanade can easily converge to the ground truth. With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes. We share the datasets, code, and demo video online

关键词： computer vision Shape Machine learning Linear programming Feature extraction Iterative algorithms Sensors

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Spatio-temporal Contrastive Domain Adaptation for Action recognition

Spatio-temporal Contrastive Domain Adaptation for Action Rec...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Song, Xiaolin Zhao, Sicheng Yang, Jingyu Yue, Huanjing Xu, Pengfei Hu, Runbo Chai, Hua Tianjin Univ Tianjin Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA Didi Chuxing Beijing Peoples R China

ISBN: (纸本)9781665445092

Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition.

关键词： Measurement Bridges Adaptation models computer vision Benchmark testing Data models pattern recognition

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Shelf-Supervised Mesh Prediction in the Wild

Shelf-Supervised Mesh Prediction in the Wild

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ye, Yufei Tulsiani, Shubham Gupta, Abhinav Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res Pittsburgh PA USA

ISBN: (纸本)9781665445092

We aim to infer 3D shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems (i.e. `shelf-supervised'). We first infer a volumetric representation in a canonical frame, along with the camera pose. We enforce the representation geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from image collections and per-instance reconstruction in finer details. We examine the method on both synthetic and the real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.

关键词： Visualization Three-dimensional displays Shape Scalability Lighting Predictive models Rendering (computer graphics)

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StereoPIFu: Depth Aware Clothed Human Digitization via Stereo vision

StereoPIFu: Depth Aware Clothed Human Digitization via Stere...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hong, Yang Zhang, Juyong Jiang, Boyi Guo, Yudong Liu, Ligang Bao, Hujun Univ Sci & Technol China Hefei Anhui Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction. Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details. Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality. Consequently, our StereoPIFu can naturally infer the human body's spatial location in camera space and maintain the correct relative position of different parts of the human body, which enables our method to capture human performance. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction, which is demonstrated by extensive experimental results.

关键词： Geometry Surface reconstruction computer vision Three-dimensional displays Shape Robustness Stereo vision

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DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Da...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wolf, Valentin Lugmayr, Andreas Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a novel unpaired learning formulation for conditional normalizing flows. We model the degradation process in the latent space of a shared flow encoder-decoder network. This allows us to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions. We validate our DeFlow formulation on the task of joint image restoration and super-resolution. The models trained with the synthetic data generated by DeFlow outperform previous learnable approaches on three recent datasets.

关键词： Degradation Superresolution Training data Stochastic processes Data models Image restoration pattern recognition

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DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency

DiLiGenRT: A Photometric Stereo Dataset with Quantified Roug...

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conference on computer vision and pattern recognition (CVPR)

作者： Heng Guo Jieji Ren Feishi Wang Boxin Shi Mingjun Ren Yasuyuki Matsushita School of Artificial Intelligence Beijing University of Posts and Telecommunications School of Mechanical Engineering Shanghai Jiao Tong University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University Graduate School of Information Science and Technology Osaka University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT, the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Transluency, Unlike qualitative and semantic labels, such as “diffuse” and “specular,” that have been used in previous datasets, our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition, it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://***/***.

关键词： Reflectivity computer vision Systematics Semantics Benchmark testing pattern recognition Manufacturing

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