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Geometric Structure Preserving Warp for Natural Image Stitching

Geometric Structure Preserving Warp for Natural Image Stitch...

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

作者： Du, Peng Ning, Jifeng Cui, Jiguang Huang, Shaoli Wang, Xinchao Wang, Jiaxin Northwest A&F Univ Xianyang Peoples R China Tencent AI Lab Bellevue WA USA Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Preserving geometric structures in the scene plays a vital role in image stitching. However, most of the existing methods ignore the large-scale layouts reflected by straight lines or curves, decreasing overall stitching quality. To address this issue, this work presents a structure-preserving stitching approach that produces images with natural visual effects and less distortion. Our method first employs deep learning-based edge detection to extract various types of large-scale edges. Then, the extracted edges are sampled to construct multiple groups of triangles to represent geometric structures. Meanwhile, a GEometric Structure preserving (GES) energy term is introduced to make these triangles undergo similarity transformation. Further, an optimized GES energy term is presented to reasonably determine the weights of the sampling points on the geometric structure, and the term is added into the Global Similarity Prior (GSP) stitching model called GES-GSP to achieve a smooth transition between local alignment and geometric structure preservation. The effectiveness of GES-GSP is validated through comprehensive experiments on a stitching dataset. The experimental results show that the proposed method outperforms several state-of-the-art methods in geometric structure preservation and obtains more natural stitching results. The code and dataset are available at https://***/flowerDuo/GES-GSP-Stitching.

关键词： computer vision Codes Image edge detection Layout Germanium Visual effects Distortion

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Unsupervised Pre-training for Temporal Action Localization Tasks

Unsupervised Pre-training for Temporal Action Localization T...

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

作者： Zhang, Can Yang, Tianyu Weng, Junwu Cao, Meng Wang, Jue Zou, Yuexian Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Tencent AI Lab Bellevue WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised video representation learning has made remarkable achievements in recent years. However, most existing methods are designed and optimized for video classification. These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization. To bridge this gap, we make the first attempt to propose a self-supervised pretext task, coined as Pseudo Action Localization (PAL) to Unsupervisedly Pre-train feature encoders for Temporal Action Localization tasks (UP-TAL). Specifically, we first randomly select temporal regions, each of which contains multiple clips, from one video as pseudo actions and then paste them onto different temporal positions of the other two videos. The pretext task is to align the features of pasted pseudo action regions from two synthetic videos and maximize the agreement between them. Compared to the existing unsupervised video representation learning approaches, our PAL adapts better to downstream TAL tasks by introducing a temporal equivariant contrastive learning paradigm in a temporally dense and scale-aware manner. Extensive experiments show that PAL can utilize large-scale unlabeled video data to significantly boost the performance of existing TAL methods. Our codes and models will be made publicly available at https://***/zhang-can/UP-TAL.

关键词： Location awareness Representation learning Bridges computer vision Adaptation models Codes Computational modeling

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Glance and Gaze: Inferring Action-aware Points for One-Stage Human-Object Interaction Detection

Glance and Gaze: Inferring Action-aware Points for One-Stage...

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

作者： Zhong, Xubin Qu, Xian Ding, Changxing Tao, Dacheng South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Modern human-object interaction (HOI) detection approaches can be divided into one-stage methods and two-stage ones. One-stage models are more efficient due to their straightforward architectures, but the two-stage models are still advantageous in accuracy. Existing one-stage models usually begin by detecting predefined interaction areas or points, and then attend to these areas only for interaction prediction;therefore, they lack reasoning steps that dynamically search for discriminative cues. In this paper, we propose a novel one-stage method, namely Glance and Gaze Network (GGNet), which adaptively models a set of action-aware points (ActPoints) via glance and gaze steps. The glance step quickly determines whether each pixel in the feature maps is an interaction point. The gaze step leverages feature maps produced by the glance step to adaptively infer ActPoints around each pixel in a progressive manner. Features of the refined ActPoints are aggregated for interaction prediction. Moreover, we design an action-aware approach that effectively matches each detected interaction with its associated human-object pair, along with a novel hard negative attentive loss to improve the optimization of GGNet. All the above operations are conducted simultaneously and efficiently for all pixels in the feature maps. Finally, GGNet outperforms state-of-the-art methods by significant margins on both V-COCO and HICO-DET benchmarks. Code of GGNet is available at https://***/SherlockHolmes221/GGNet.

关键词： Adaptation models Visualization computer vision Codes Color Predictive models Feature extraction

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Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector

Towards Better Plasticity-Stability Trade-off in Incremental...

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

作者： Lin, Guoliang Chu, Hanlu Lai, Hanjiang Sun Yat Sen Univ Guangzhou Guangdong Peoples R China South China Normal Univ Guangzhou Guangdong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is referring to the ability to learn new knowledge, and stability retains the knowledge of previous tasks. Many methods tackle this problem by storing previous samples, while in some applications, training data from previous tasks cannot be legally stored. In this work, we propose to employ mode connectivity in loss landscapes to achieve better plasticity-stability tradeoff without any previous samples. We give an analysis of why and how to connect two independently optimized optima of networks, null-space projection for previous tasks and simple SGD for the current task, can attain a meaningful balance between preserving already learned knowledge and granting sufficient flexibility for learning a new task. This analysis of mode connectivity also provides us a new perspective and technology to control the trade-off between plasticity and stability. We evaluate the proposed method on several benchmark datasets. The results indicate our simple method can achieve notable improvement, and perform well on both the past and current tasks. On 10-split-CIFAR-100 task, our method achieves 79.79% accuracy, which is 6.02% higher. Our method also achieves 6.33% higher accuracy on TinyImageNet. Code is available at https://***/lingl1024/Connector.

关键词： Connectors Knowledge engineering computer vision Upper bound Codes Neural networks Training data

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Hierarchical Modular Network for Video Captioning

Hierarchical Modular Network for Video Captioning

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

作者： Ye, Hanhua Li, Guorong Qi, Yuankai Wang, Shuhui Huang, Qingming Yang, Ming-Hsuan Univ Chinese Acad Sci Beijing Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Chinese Acad Sci Key Lab Intell Info Proc Inst Comput Tech Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Calif Merced Merced CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video captioning aims to generate natural language descriptions according to the content, where representation learning plays a crucial role. Existing methods are mainly developed within the supervised learning framework via word-by-word comparison of the generated caption against the ground-truth text without fully exploiting linguistic semantics. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics from three levels before generating captions. In particular, the hierarchy is composed of: (I) Entity level, which highlights objects that are most likely to be mentioned in captions. (II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions. (III) Sentence level, which learns the global semantic representation and is supervised by the whole caption. Each level is implemented by one module. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on the two widely-used benchmarks: MSVD 104.0% and MSR-VTT 51.5% in CIDEr score. Code will be made available at https://***/MarcusNerva/HMN.

关键词： Bridges Representation learning Visualization computer vision Semantics Supervised learning Linguistics

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Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers

Panoptic SegFormer: Delving Deeper into Panoptic Segmentatio...

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

作者： Li, Zhiqi Wang, Wenhai Xie, Enze Yu, Zhiding Anandkumar, Anima Alvarez, Jose M. Luo, Ping Lu, Tong Nanjing Univ Nanjing Jiangsu Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China NVIDIA Santa Clara CA USA CALTECH Pasadena CA 91125 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved post-processing method. We also use Deformable DETR to efficiently process multi-scale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our postprocessing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO test-dev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods.

关键词： Training Image segmentation computer vision Costs Semantics Interference Transformers

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RMT: Retentive Networks Meet vision Transformers

RMT: Retentive Networks Meet Vision Transformers

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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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Learning to Prompt for Open-Vocabulary Object Detection with vision-Language Model

Learning to Prompt for Open-Vocabulary Object Detection with...

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

作者： Du, Yu Wei, Fangyun Zhang, Zihe Shi, Miaojing Gao, Yue Li, Guoqi Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Kings Coll London London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as the region classifier to supervise the training of a detector. The key element that leads to the success of this model is the proper prompt, which requires careful words tuning and ingenious design. To avoid laborious prompt engineering, there are some prompt representation learning methods being proposed for the image classification task, which however can only be sub-optimal solutions when applied to the detection task. In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model. Different from the previous classification-oriented methods, DetPro has two highlights: 1) a background interpretation scheme to include the proposals in image background into the prompt training;2) a context grading scheme to separate proposals in image foreground for tailored prompt training. We assemble DetPro with ViLD, a recent state-of-the-art openworld object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the baseline ViLD [7] in all settings, e.g., +3.4 AP(box )and +3.0 AP(mas)(k )improvements on the novel classes of LVIS. Code and models are available at https://***/dyable/detpro.

关键词： Training Representation learning Visualization Transfer learning Object detection Detectors pattern recognition

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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised M...

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

作者： Liu, Fengbei Tian, Yu Chen, Yuanhong Liu, Yuyuan Belagiannis, Vasileios Carneiro, Gustavo Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Univ Ulm Ulm Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle unbalanced learning (because of the high variance in disease prevalence). One strategy to explore in SSL MIA is based on the pseudo labelling strategy, but it has a few shortcomings. Pseudo-labelling has in general lower accuracy than consistency learning, it is not specifically design for both multi-class and multi-label problems, and it can be challenged by imbalanced learning. In this paper;unlike traditional methods that select confident pseudo label by threshold, we propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL), which introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers (improving pseudo label accuracy). We run extensive experiments to evaluate ACPL on two public medical image classification benchmarks: Chest X-Ray14 for thorax disease multi-label classification and ISIC2018 for skin lesion multi-class classification. Our method outperforms previous SOTA SSL methods on both datasets(12).

关键词： Training Semisupervised learning Thorax Skin pattern recognition Lesions Medical diagnostic imaging

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Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Bias Mimicking: A Simple Sampling Approach for Bias Mitigati...

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

作者： Qraitem, Maan Saenko, Kate Plummer, Bryan A. Boston Univ Boston MA 02215 USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Prior work has shown that Visual recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyper-parameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class c bias distribution, i.e. PD(B| Y = c) is mimicked across every c ' not equal c, then Y and B are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https: //***/mqraitem/Bias-Mimicking

关键词： accountability accountability ethics in vision fairness privacy Transparency

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