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

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Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding

Incremental Transformer Structure Enhanced Image Inpainting ...

引用

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

作者： Dong, Qiaole Cao, Chenjie Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image inpainting has made significant advances in recent years. However, it is still challenging to recover corrupted images with both vivid textures and reasonable structures. Some specific methods only tackle regular textures while losing holistic structures due to the limited receptive fields of convolutional neural networks (CNNs). On the other hand, attention-based models can learn better long-range dependency for the structure recovery, but they are limited by the heavy computation for inference with large image sizes. To address these issues, we propose to leverage an additional structure restorer to facilitate the image inpainting incrementally. The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space. Such a grayscale space is easy to be upsampled to larger scales to convey correct structural information. Our structure restorer can be integrated with other pretrained inpainting models efficiently with the zero-initialized residual addition. Furthermore, a masking positional encoding strategy is utilized to improve the performance with large irregular masks. Extensive experiments on various datasets validate the efficacy of our model compared with other competitors.

关键词： computer vision Image resolution Image coding Computational modeling Gray-scale Transformers Encoding

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Revisiting Superpixels for Active Learning in Semantic Segmentation with Realistic Annotation Costs

Revisiting Superpixels for Active Learning in Semantic Segme...

引用

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

作者： Cai, Lile Xu, Xun Liew, Jun Hao Foo, Chuan Sheng Inst Infocomm Res Singapore Singapore Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

State-of-the-art methods for semantic segmentation are based on deep neural networks that are known to be data-hungry. Region-based active learning has shown to be a promising method for reducing data annotation costs. A key design choice for region-based AL is whether to use regularly-shaped regions (e.g., rectangles) or irregularly-shaped region (e.g., superpixels). In this work, we address this question under realistic, click-based measurement of annotation costs. In particular, we revisit the use of super-pixels and demonstrate that the inappropriate choice of cost measure (e.g., the percentage of labeled pixels), may cause the effectiveness of the superpixel-based approach to be under-estimated. We benchmark the superpixel-based approach against the traditional "rectangle+polygon"-based approach with annotation cost measured in clicks, and show that the former outperforms on both Cityscapes and PASCAL VOC. We further propose a class-balanced acquisition function to boost the performance of the superpixel-based approach and demonstrate its effectiveness on the evaluation datasets. Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.

关键词： Deep learning computer vision Costs Annotations Atmospheric measurements Semantics Benchmark testing

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Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

Sylph: A Hypernetwork Framework for Incremental Few-shot Obj...

引用

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

作者： Yin, Li Perez-Rua, Juan M. Liang, Kevin J. Meta AI Menlo Pk CA 94025 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the challenging incremental few-shot object detection (iFSD) setting. Recently, hypernetwork-based approaches have been studied in the context of continuous and finetune-free iFSD with limited success. We take a closer look at important design choices of such methods, leading to several key improvements and resulting in a more accurate and flexible framework, which we call Sylph. In particular, we demonstrate the effectiveness of decoupling object classification from localization by leveraging a base detector that is pretrained for class-agnostic localization on large-scale dataset. Contrary to what previous results have suggested, we show that with a carefully designed class-conditional hypernetwork, finetune-free iFSD can be highly effective, especially when a large number of base categories with abundant data are available for metatraining, almost approaching alternatives that undergo testtime-training. This result is even more significant considering its many practical advantages: (1) incrementally learning new classes in sequence without additional training, (2) detecting both novel and seen classes in a single pass, and (3) no forgetting of previously seen classes. We benchmark our model on both COCO and LVIS, reporting as high as 17% AP on the long-tail rare classes on LVIS, indicating the promise of hypernetwork-based iFSD.

关键词： Training Location awareness Deep learning computer vision Fuses Object detection Detectors

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Few-Shot Head Swapping in the Wild

Few-Shot Head Swapping in the Wild

引用

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

作者： Shu, Changyong Wu, Hemao Zhou, Hang Liu, Jiaming Hong, Zhibin Ding, Changxing Han, Junyu Liu, Jingtuo Ding, Errui Wang, Jingdong Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherently challenging due to its unique needs in head modeling and background blending. In this paper, we present the Head Swapper (HeSer), which achieves few-shot head swapping in the wild through two delicately designed modules. Firstly, a Head2Head Aligner is devised to holistically migrate pose and expression information from the target to the source head by examining multi-scale information. Secondly, to tackle the challenges of skin color variations and head-background mismatches in the swapping procedure, a Head2Scene Blender is introduced to simultaneously modify facial skin color and fill mismatched gaps on the background around the head. Particularly, seamless blending is achieved with the help of a Semantic-Guided Color Reference Creation procedure and a Blending UNet. Extensive experiments demonstrate that the pro- posed method produces superior head swapping results on a variety of scenes.

关键词： computer vision Head Image color analysis Face recognition Entertainment industry Skin Task analysis

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RMT: Retentive Networks Meet vision Transformers

RMT: Retentive Networks Meet Vision Transformers

引用

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

作者： Fan, Qihang Huang, Huaibo Chen, Mingrui Liu, Hongmin He, Ran Chinese Acad Sci Inst Automat MAIS & CRIPAC Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However, the core component of ViT, Self-Attention, lacks explicit spatial priors and bears a quadratic computational complexity, thereby constraining the applicability of ViT. To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes. Specifically, we extend the RetNet's temporal decay mechanism to the spatial domain, and propose a spatial decay matrix based on the Manhattan distance to introduce the explicit spatial prior to Self-Attention. Additionally, an attention decomposition form that adeptly adapts to explicit spatial prior is proposed, aiming to reduce the computational burden of modeling global information without disrupting the spatial decay matrix. Based on the spatial decay matrix and the attention decomposition form, we can flexibly integrate explicit spatial prior into the vision backbone with linear complexity. Extensive experiments demonstrate that RMT exhibits exceptional performance across various vision tasks. Specifically, without extra training data, RMT achieves 84.8% and 86.1% top-1 acc on ImageNet-1k with 27M/4.5GFLOPs and 96M/18.2GFLOPs. For downstream tasks, RMT achieves 54.5 box AP and 47.2 mask AP on the COCO detection task, and 52.8 mIoU on the ADE20K se-mantic segmentation task.

关键词： vision Transformer

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Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution

Details or Artifacts: A Locally Discriminative Learning Appr...

引用

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

作者： Liang, Jie Zeng, Hui Zhang, Lei HongKong Polytech Univ Hong Kong Peoples R China OPPO Res Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts. Based on the observation that the local statistics (e.g., residual variance) of artifact areas are often different from the areas of perceptually friendly details, we develop a framework to discriminate between GAN-generated artifacts and realistic details, and consequently generate an artifact map to regularize and stabilize the model training process. Our proposed locally discriminative learning (LDL) method is simple yet effective, which can be easily plugged in off-the-shelf SISR methods and boost their performance. Experiments demonstrate that LDL outperforms the state-of-the-art GAN based SISR methods, achieving not only higher reconstruction accuracy but also superior perceptual quality on both synthetic and real-world datasets. Codes and models are available at https://***/csjliang/LDL.

关键词： Training Visualization computer vision Codes Computational modeling Superresolution Generative adversarial networks

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Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

Weakly Supervised Semantic Segmentation by Pixel-to-Prototyp...

引用

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

作者： Du, Ye Fu, Zehua Liu, Qingjie Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation. In this study, we propose weakly-supervised pixel-to-prototype contrast that can provide pixel-level supervisory signals to narrow the gap. Guided by two intuitive priors, our method is executed across different views and within per single view of an image, aiming to impose cross-view feature semantic consistency regularization and facilitate intra(inter)-class compactness(dispersion) of the feature space. Our method can be seamlessly incorporated into existing WSSS models without any changes to the base networks and does not incur any extra inference burden. Extensive experiments manifest that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness. Specifically, built on top of SEAM, we improve the initial seed mIoU on PASCAL VOC 2012 from 55.4% to 61.5%. Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70.8% to 73.6%, achieving new state-of-the-art.

关键词： Image segmentation computer vision Shape Semantics Prototypes Estimation pattern recognition

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Efficient Image Super-Resolution with Collapsible Linear Blocks

Efficient Image Super-Resolution with Collapsible Linear Blo...

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

作者： Wang, Li Li, Dong Tian, Lu Shan, Yi Adv Micro Devices Inc Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we propose a simple but effective architecture for fast and accurate single image super-resolution. Unlike other compact image super-resolution methods based on hand-crafted designs, we first apply coarse-grained pruning for network acceleration, and then introduce collapsible linear blocks to recover the representative ability of the pruned network. Specifically, each collapsible linear block has a multi-branch topology during training, and can be equivalently replaced with a single convolution in the inference stage. Such decoupling of the training-time and inference-time architecture is implemented via a structural re-parameterization technique, leading to improved representation without introducing extra computation costs. Additionally, we adopt a two-stage training mechanism with progressively larger patch sizes to facilitate the optimization procedure. We evaluate the proposed method on the NTIRE 2022 Efficient Image Super-Resolution Challenge and achieve a good trade-off between latency and accuracy. Particularly, under the condition of limited inference time (<= 49.42ms) and parameter amount (<= 0.894M), our solution obtains the best fidelity results in terms of PSNR, i.e., 29.05dB and 28.75dB on the DIV2K validation and test sets, respectively.

关键词： Training Costs Network topology Convolution conferences Superresolution computer architecture

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ObjectFormer for Image Manipulation Detection and Localization

ObjectFormer for Image Manipulation Detection and Localizati...

引用

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

作者： Wang, Junke Wu, Zuxuan Chen, Jingjing Han, Xintong Shrivastava, Abhinav Lim, Ser-Nam Jiang, Yu-Gang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Huya Inc Guangzhou Peoples R China Univ Maryland College Pk MD 20742 USA Meta AI New York NY USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.

关键词： Location awareness Visualization computer vision Frequency-domain analysis Computational modeling Prototypes Feature extraction

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Self-Supervised Learning for Semi-Supervised Temporal Action Proposal

Self-Supervised Learning for Semi-Supervised Temporal Action...

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

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Shao, Yuanjie Gao, Changxin Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation. Particularly, we design an effective Self-supervised Semi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains two crucial branches, i.e., temporal-aware semi-supervised branch and relation-aware self-supervised branch. The semi-supervised branch improves the proposal model by introducing two temporal perturbations, i.e., temporal feature shift and temporal feature flip, in the mean teacher framework. The self-supervised branch defines two pretext tasks, including masked feature reconstruction and clip-order prediction, to learn the relation of temporal clues. By this means, SSTAP can better explore unlabeled videos, and improve the discriminative abilities of learned action features. We extensively evaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The experimental results demonstrate that SSTAP significantly outperforms state-of-the-art semi-supervised methods and even matches fully-supervised methods.

关键词： computer vision Codes Perturbation methods pattern recognition Proposals Task analysis

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