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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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ZBS: Zero-Shot Background Subtraction via Instance-Level Background Modeling and Foreground Selection

ZBS: Zero-Shot Background Subtraction via Instance-Level Bac...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yongqi An Xu Zhao Tao Yu Haiyun Gu Chaoyang Zhao Ming Tang Jinqiao Wang National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsuper-vised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://***/CASIA-IVA-Lab/ZBS.

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Uncertainty-guided Model Generalization to Unseen Domains

Uncertainty-guided Model Generalization to Unseen Domains

引用

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

作者： Qiao, Fengchun Peng, Xi Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9781665445092

We study a worst-case scenario in generalization: Out-of-domain generalization from a single source. The goal is to learn a robust model from a single source and expect it to generalize over many unknown distributions. This challenging problem has been seldom investigated while existing solutions suffer from various limitations. In this paper, we propose a new solution. The key idea is to augment the source capacity in both input and label spaces, while the augmentation is guided by uncertainty assessment. To the best of our knowledge, this is the first work to (1) access the generalization uncertainty from a single source and (2) leverage it to guide both input and label augmentation for robust generalization. The model training and deployment are effectively organized in a Bayesian meta-learning framework. We conduct extensive comparisons and ablation study to validate our approach. The results prove our superior performance in a wide scope of tasks including image classification, semantic segmentation, text classification, and speech recognition.

关键词： Training Image segmentation Uncertainty Perturbation methods Text categorization Semantics Speech recognition

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RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction

RepMode: Learning to Re-Parameterize Diverse Experts for Sub...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Donghao Zhou Chunbin Gu Junde Xu Furui Liu Qiong Wang Guangyong Chen Pheng-Ann Heng Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The Chinese University of Hong Kong Zhejiang Lab

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

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Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing

Exploiting Spatial Dimensions of Latent in GAN for Real-time...

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

作者： Kim, Hyunsu Choi, Yunjey Kim, Junho Yoo, Sungjoo Uh, Youngjung NAVER AI Lab Seoul South Korea Seoul Natl Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN.

关键词： Interpolation computer vision Codes Computational modeling Modulation Generative adversarial networks Real-time systems

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Glancing at the Patch: Anomaly Localization with Global and Local Feature Comparison

Glancing at the Patch: Anomaly Localization with Global and ...

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

作者： Wang, Shenzhi Wu, Liwei Cui, Lei Shen, Yujun SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Anomaly localization, with the purpose to segment the anomalous regions within images, is challenging due to the large variety of anomaly types. Existing methods typically train deep models by treating the entire image as a whole yet put little effort into learning the local distribution, which is vital for this pixel-precise task. In this work, we propose an unsupervised patch-based approach that gives due consideration to both the global and local information. More concretely, we employ a Local-Net and Global-Net to extract features from any individual patch and its surrounding respectively. Global-Net is trained with the purpose to mimic the local feature such that we can easily detect an abnormal patch when its feature mismatches that from the context. We further introduce an Inconsistency Anomaly Detection (IAD) head and a Distortion Anomaly Detection (DAD) head to sufficiently spot the discrepancy between global and local features. A scoring function derived from the multi-head design facilitates high-precision anomaly localization. Extensive experiments on a couple of real-world datasets suggest that our approach outperforms state-of-the-art competitors by a sufficiently large margin.

关键词： Location awareness Image segmentation computer vision Head Feature extraction Distortion pattern recognition

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LASR: Learning Articulated Shape Reconstruction from a Monocular Video

LASR: Learning Articulated Shape Reconstruction from a Monoc...

引用

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

作者： Yang, Gengshan Sun, Deqing Jampani, Varun Vlasic, Daniel Cole, Forrester Chang, Huiwen Ramanan, Deva Freeman, William T. Liu, Ce Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved great success in modeling the "closed world" of known object categories, they cannot well handle the "open-world" of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Our code is available at ***.

关键词： computer vision Three-dimensional displays Codes Shape Dogs Cameras pattern recognition

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DegAE: A New Pretraining Paradigm for Low-Level vision

DegAE: A New Pretraining Paradigm for Low-Level Vision

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conference on computer vision and pattern recognition (cvpr)

作者： Yihao Liu Jingwen He Jinjin Gu Xiangtao Kong Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The University of Sydney

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acqui-sition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (De-gAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tai-lored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

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D²IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

D<SUP>2</SUP>IM-Net: Learning Detail Disentangled Implicit F...

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

作者： Li, Manyi Zhang, Hao Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined (DIM)-I-2-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

关键词： Surface reconstruction computer vision Three-dimensional displays Laplace equations Shape Decoding pattern recognition

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Representation Learning via Global Temporal Alignment and Cycle-Consistency

Representation Learning via Global Temporal Alignment and Cy...

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

作者： Hadji, Isma Derpanis, Konstantinos G. Jepson, Allan D. Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle-consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.

关键词： computer vision Three-dimensional displays Probabilistic logic Routing pattern recognition Synchronization Task analysis

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DeAR: Debiasing vision-Language Models with Additive Residuals

DeAR: Debiasing Vision-Language Models with Additive Residua...

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conference on computer vision and pattern recognition (cvpr)

作者： Ashish Seth Mayur Hemani Chirag Agarwal IIT Madras India Adobe Inc.

Large pre-trained vision-language models (VLMs) reduce the time for developing predictive models for various vision-grounded language downstream tasks by providing rich, adaptable image and text representations. However, these models suffer from societal biases owing to the skewed distribution of various identity groups in the training data. These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications. In this work, we present Dear(Debiasing with Additive Residuals), a novel debiasing method that learns additive residual image representations to offset the original representations, ensuring fair output representations. In doing so, it reduces the ability of the representations to distinguish between the different identity groups. Further, we observe that the current fairness tests are performed on limited face image datasets that fail to indicate why a specific text concept should/should not apply to them. To bridge this gap and better evaluate Dear,we introduce the Protected Attribute Tag Association (pata)dataset - a new context-based bias benchmarking dataset for evaluating the fairness of large pre-trained VLMs. Additionally, Pataprovides visual context for a diverse human population in different scenarios with both positive and negative connotations. Experimental results for fairness and zero-shot performance preservation using multiple datasets demonstrate the efficacy of our framework. The dataset is released here.

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