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Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

Non-Salient Region Object Mining for Weakly Supervised Seman...

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

作者： Yao, Yazhou Chen, Tao Xie, Guo-Sen Zhang, Chuanyi Shen, Fumin Wu, Qi Tang, Zhenmin Zhang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

关键词： Image segmentation computer vision Codes Annotations Image edge detection Semantics Cognition

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Pushing the Performance Limit of Scene Text Recognizer without Human Annotation

Pushing the Performance Limit of Scene Text Recognizer witho...

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

作者： Zheng, Caiyuan Li, Hui Rhee, Seon-Min Han, Seungju Han, Jae-Joon Wang, Peng Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Natl Engn Lab Integrated Aerosp Ground Ocean Big Xian Peoples R China Samsung R&D Inst China Xian SRCX Xian South Korea Samsung Adv Inst Technol SAIT Suwon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a filly supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-to-synthetic domain gap the restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR.

关键词： Training Adaptation models computer vision Costs Text recognition Annotations Benchmark testing

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BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations

BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotat...

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

作者： Li, Daiqing Ling, Huan Kim, Seung Wook Kreis, Karsten Fidler, Sanja Torralba, Antonio NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Bengaluru India MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Annotating images with pixel-wise labels is a time consuming and costly process. Recently, DatasetGAN [78] showcased a promising alternative - to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity. We take image samples from the class-conditional generative model BigGAN [5] trained on ImageNet, and manually annotate only 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn Big GAN into a labeled dataset generator. We further show that VQGAN [18] can similarly serve as a dataset generator, leveraging the already annotated data. We create a new ImageNet benchmark by labeling an additional set of real images and evaluate segmentation performance in a variety of settings. Through an extensive ablation study, we show big gains in leveraging a large generated dataset to train different supervised and self-supervised backbone models on pixel-wise tasks. Furthermore, we demonstrate that using our synthesized datasets for pre-training leads to improvements over standard ImageNet pre-training on several downstream datasets, such as PASCAL-VOC, MS-COCO, Cityscapes and chest X-ray, as well as tasks (detection, segmentation). Our benchmark will be made public and maintain a leaderboard for this challenging task. Project Page: https://***/big-datasetgan/

关键词： Training Image segmentation Benchmark testing Generative adversarial networks Generators pattern recognition Labeling

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End-to-end representation learning for Correlation Filter based tracking 30

End-to-end representation learning for Correlation Filter ba...

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30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Valmadre, Jack Bertinetto, Luca Henriques, Joao Vedaldi, Andrea Torr, Philip H. S. Univ Oxford Oxford England

ISBN: (纸本)9781538604571

The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.

关键词： Correlation Training computer architecture Object tracking Filtering algorithms Optimization Target tracking

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End-to-End High-Risk Tackle Detection System for Rugby

End-to-End High-Risk Tackle Detection System for Rugby

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

作者： Nonaka, Naoki Fujihira, Ryo Nishio, Monami Murakami, Hidetaka Tajima, Takuya Yamada, Mutsuo Maeda, Akira Seita, Jun RIKEN Informat R&D & Strategy Headquarters Adv Data Sci Project Tokyo Japan Murakami Surg Hosp Nagasaki Japan Univ Miyazaki Fac Med Miyazaki Japan Ryutsu Keizai Univ Fac Hlth & Sport Sci Ryugasaki Japan Hakata Knee & Sports Clin Fukuoka Japan Kurume Univ Fac Human Hlth Kurume Fukuoka Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reducing risk of severe injury such as concussion is a high priority for any contact sports. In rugby, Head Injury Assessment (HIA) protocol has been introduced to identify and protect players showing symptoms of concussion and having potential risk of concussion. However, on-field decisions by officials are sometimes difficult and subjective, and HIA is affordable only for elite leagues since it requires medical specialists. To make rugby matches more safe, we aim to develop a system to detect high-risk tackles, potential triggers of concussion, based on deep learning models. Our system takes rugby match video, then first identifies frame with tackle, subsequently detects location of tackle and estimate pose of the ball carrier and the tackler, and finally evaluate the risk of tackle using posture pair of players. Among the model combinations we have examined, the best performance was achieved with the combination of ResNet (2+1)D as tackle frame selection model, RetinaNet as tackle detection model and CenterTrack as pose estimation model. Evaluation using test data, a set of short clips from broadcasted rugby match videos, showed our system was able to detect 50% of high-risk tackles without any human intervention. This result opens a path for automated systems to detect high-risk events, leading to less expensive and more objective monitoring not only for rugby but also for any contact sports.

关键词： Deep learning computer vision Protocols Head conferences Pose estimation pattern recognition

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Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

Propagate Yourself: Exploring Pixel-Level Consistency for Un...

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

作者： Xie, Zhenda Lin, Yutong Zhang, Zheng Cao, Yue Lin, Stephen Hu, Han Tsinghua Univ Beijing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions. In this paper, we introduce pixel-level pretext tasks for learning dense feature representations. The first task directly applies contrastive learning at the pixel level. We additionally propose a pixel-to-propagation consistency task that produces better results, even surpassing the state-of-the-art approaches by a large margin. Specifically, it achieves 60.2 AP, 41.4 / 40.5 mAP and 77.2 mIoU when transferred to Pascal VOC object detection (C4), COCO object detection (FPN / C4) and Cityscapes semantic segmentation using a ResNet-50 backbone network, which are 2.6 AP, 0.8 / 1.0 mAP and 1.0 mIoU better than the previous best methods built on instance-level contrastive learning. Moreover, the pixel-level pretext tasks are found to be effective for pretraining not only regular backbone networks but also head networks used for dense downstream tasks, and are complementary to instance-level contrastive methods. These results demonstrate the strong potential of defining pretext tasks at the pixel level, and suggest a new path forward in unsupervised visual representation learning. Code is available at https://***/zdaxie/PixPro.

关键词： Learning systems Visualization computer vision Head Codes Semantics Object detection

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SPIN: Simultaneous Perception, Interaction and Navigation

SPIN: Simultaneous Perception, Interaction and Navigation

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

作者： Uppal, Shagun Agarwal, Ananye Xiong, Haoyu Shaw, Kenneth Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation, a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting, there are a plethora of challenges in developing these systems such as coordination between the base and arm, reliance on onboard perception for perceiving and interacting with the environment, and most importantly, simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors, delays in decision-making, and no whole-body coordination. In this work, we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see, more specifically - to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also, choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://***

关键词： Active vision End-to-end learning Mobile Manipulation Whole-body coordination

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A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

A Large-scale Comprehensive Dataset and Copy-overlap Aware E...

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

作者： He, Sifeng Yang, Xudong Jiang, Chen Liang, Gang Zhang, Wei Pan, Tan Wang, Qing Xu, Furong Li, Chunguang Liu, Jingxiong Xu, Hui Huang, Kaiming Cheng, Yuan Qian, Feng Zhang, Xiaobo Yang, Lei Ant Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://***/alipay/VCSL.

关键词： Measurement Location awareness computer vision Protocols Video on demand Current measurement Benchmark testing

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Learning the Superpixel in a Non-iterative and Lifelong Manner

Learning the Superpixel in a Non-iterative and Lifelong Mann...

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

作者： Zhu, Lei She, Qi Zhang, Bin Lu, Yanye Lu, Zhilin Li, Duo Hu, Jie Peking Univ Hlth Sci Ctr Inst Med Technol Beijing Peoples R China Bytedance AI Lab Beijing Peoples R China Peking Univ Dept Biomed Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Peking Univ Inst Biomed Engn Shenzhen Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. Although some works use the Convolution Neural Network (CNN) to generate high-quality superpixel, we challenge the design principles of these networks, specifically for their dependence on manual labels and excess computation resources, which limits their flexibility compared with the traditional unsupervised segmentation methods. We target at redefining the CNN-based superpixel segmentation as a lifelong clustering task and propose an unsupervised CNN-based method called LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong manner without any manual labels. Specifically, a lightweight feature embedder is proposed for LNS-Net to efficiently generate the cluster-friendly features. With those features, seed nodes can be automatically assigned to cluster pixels in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially lifelong learning by rescaling the gradient of weight based on both channel and spatial context to avoid overfitting. Experiments show that the proposed LNS-Net achieves significantly better performance on three benchmarks with nearly ten times lower complexity compared with other state-of-the-art methods.

关键词： Image segmentation Convolution Neural networks Manuals Benchmark testing pattern recognition Finite element analysis

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Ranking-Based Siamese Visual Tracking

Ranking-Based Siamese Visual Tracking

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

作者： Tang, Feng Ling, Qiang Univ Sci & Technol China Dept Automat Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. The code and raw results are available at https: //***/sansanfree/RBO.

关键词： Location awareness Visualization computer vision Tracking Prediction algorithms Inference algorithms Classification algorithms

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