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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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ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation

ABMDRNet: Adaptive-weighted Bi-directional Modality Differen...

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

作者： Zhang, Qiang Zhao, Shenlu Luo, Yongjiang Zhang, Dingwen Huang, Nianchang Han, Jungong Xidian Univ Sch Mechano Elect Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China Aberystwyth Univ Comp Sci Dept Aberystwyth Dyfed Wales

ISBN: (纸本)9781665445092

Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and weighted summation, to fuse features from different modalities. These strategies, unfortunately, overlook the modality differences due to different imaging mechanisms, so that they suffer from the reduced discriminability of the fused features. To address such an issue, we propose, for the first time, the strategy of bridging-then-fusing, where the innovation lies in a novel Adaptive-weighted Bi-directional Modality Difference Reduction Network (ABMDRNet). Concretely, a Modality Difference Reduction and Fusion (MDRF) subnetwork is designed, which first employs a bi-directional image-to-image translation based method to reduce the modality differences between RGB features and thermal features, and then adaptively selects those discriminative multi-modality features for RGB-T semantic segmentation in a channel-wise weighted fusion way. Furthermore, considering the importance of contextual information in semantic segmentation, a Multi-Scale Spatial Context (MSC) module and a Multi-Scale Channel Context (MCC) module are proposed to exploit the interactions among multi-scale contextual information of cross-modality features together with their long-range dependencies along spatial and channel dimensions, respectively. Comprehensive experiments on MFNet dataset demonstrate that our method achieves new state-of-the-art results.

关键词： Image segmentation Technological innovation computer vision Fuses Semantics Lighting Imaging

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Recurrent Dynamic Embedding for Video Object Segmentation

Recurrent Dynamic Embedding for Video Object Segmentation

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

作者： Li, Mingxing Hu, Li Xiong, Zhiwei Zhang, Bang Pan, Pan Liu, Dong Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Alibaba DAMO Acad Hangzhou Peoples R China Alibaba Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However;1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably introduces lots of noise, which is not conducive to reading the most important information from the memory bank In this paper, we propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size. Specifically, we explicitly generate and update RDE by the proposed Spatio-temporal Aggregation Module (SAM), which exploits the cue of historical information. To avoid error accumulation owing to the recurrent usage of SAM, we propose an unbiased guidance loss during the training stage, which makes SAM more robust in long videos. Moreover, the predicted masks in the memory bank are inaccurate due to the inaccurate network inference, which affects the segmentation of the query frame. To address this problem, we design a novel self-correction strategy so that the network can repair the embeddings of masks with different qualities in the memory bank Extensive experiments show our method achieves the best tradeoff between performance and speed.

关键词： Training computer vision Codes Shape Memory management Object segmentation Maintenance engineering

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Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera 32

Bringing a Blurry Frame Alive at High Frame-Rate with an Eve...

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

作者： Pan, Liyuan Scheerlinck, Cedric Yu, Xin Hartley, Richard Liu, Miaomiao Dai, Yuchao Australian Natl Univ Canberra ACT Australia Australian Ctr Robot Vis Canberra ACT Australia Northwestern Polytech Univ Sch Elect & Informat Xian Shaanxi Peoples R China

ISBN: (纸本)9781728132938

Event-based cameras can measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured at a relatively low frame-rate and often suffer from motion blur. A blurry image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we are able to model the blur-generation process by associating event data to a latent image. In this paper, we propose a simple and effective approach, the Event-based Double Integral (EDI) model, to reconstruct a high frame-rate, sharp video from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable. Experimental results on both synthetic and real images demonstrate the superiority of our EDI model and optimization method in comparison to the state-of-the-art.

关键词： Computational Photography Image and Video Synthesis vision Applications and Systems

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Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values

Polarity Sampling: Quality and Diversity Control of Pre-Trai...

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

作者： Humayun, Ahmed Imtiaz Balestriero, Randall Baraniuk, Richard Rice Univ Houston TX 77251 USA Meta AI Res Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Polarity Sampling, a theoretically justified plugand-play method for controlling the generation quality and diversity of any pre-trained deep generative network (DGN). Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space distribution as a function of the product of the DGN's Jacobian singular values raised to a power rho. We dub p the polarity parameter and prove that rho focuses the DGN sampling on the modes (rho < 0) or anti-modes (rho > 0) of the DGN output-space probability distribution. We demonstrate that nonzero polarity values achieve a better precision-recall (qualitydiversity) Pareto frontier than standard methods, such as truncation, for a number of state-of-the-art DGNs. We also present quantitative and qualitative results on the improvement of overall generation quality (e.g., in terms of the Frechet Inception Distance) for a number of state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different conditional and unconditional image generation tasks. In particular;Polarity Sampling redefines the state-of-the-art for StyleGAN2 on the FFHQ Dataset to HD 2.57, StyleGAN2 on the LSUN Car Dataset to FID 2.27 and Style-GAN3 on the AFHQv2 Dataset to FID 3.95. Colab Demo.

关键词： Training Jacobian matrices Image synthesis Force Probability distribution Generators pattern recognition

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Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

Learning to Recommend Frame for Interactive Video Object Seg...

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

作者： Yin, Zhaoyuan Zheng, Jia Luo, Weixin Qian, Shenhan Zhang, Hanling Gao, Shenghua Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Manycore KooLab Rancho Cordova CA USA Meituan Grp Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China Hunan Univ Sch Design Changsha Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms.

关键词： Measurement computer vision Codes Annotations Object segmentation Reinforcement learning Markov processes

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