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PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems

PseudoProp: Robust Pseudo-Label Generation for Semi-Supervis...

引用

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

作者： Hu, Shu Liu, Chun-Hao Dutta, Jayanta Chang, Ming-Ching Lyu, Siwei Ramakrishnan, Naveen Univ Buffalo SUNY Buffalo NY USA Bosch Ctr Artificial Intelligence Sunnyvale CA 94085 USA SUNY Albany Albany NY 12222 USA Amazon Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled objects to the unlabeled ones, pseudo-labels for unlabeled objects must be generated. Although pseudo-labels have proven to improve the performance of semisupervised object detection significantly, the applications of image-based methods to video frames result in numerous miss or false detections using such generated pseudo-labels. In this paper, we propose a new approach, PseudoProp, to generate robust pseudo-labels by leveraging motion continuity in video frames. Specifically, PseudoProp uses a novel bidirectional pseudo-label propagation approach to compensate for misdetection. A feature-based fusion technique is also used to suppress inference noise. Extensive experiments on the large-scale Cityscapes dataset demonstrate that our method outperforms the state-of-the-art semi-supervised object detection methods by 7.4% on mAP(75).

关键词： Training computer vision conferences Object detection Predictive models Feature extraction pattern recognition

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Guiding Attention using Partial-Order Relationships for Image Captioning

Guiding Attention using Partial-Order Relationships for Imag...

引用

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

作者： Popattia, Murad Rafi, Muhammad Qureshi, Rizwan Nawaz, Shah Natl Univ Comp & Emerging Sci Karachi Pakistan Hamad Bin Khalifa Univ Doha Qatar Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approaches have been proposed to enhance the attention process using different feature representations. In this paper, we extend this approach by creating a guided attention network mechanism, that exploits the relationship between the visual scene and text-descriptions using spatial features from the image, high-level information from the topics, and temporal context from caption generation, which are embedded together in an ordered embedding space. A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions. The experimental results based on MSCOCO dataset shows the competitiveness of our approach, with many state-of-the-art models on various evaluation metrics.

关键词： Training Measurement Visualization computer vision conferences Semantics computer architecture

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Material Swapping for 3D Scenes using a Learnt Material Similarity Measure

Material Swapping for 3D Scenes using a Learnt Material Simi...

引用

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

作者： Perroni-Scharf, Maxine Sunkavalli, Kalyan Eisenmann, Jonathan Hold-Geoffroy, Yannick Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a method for augmenting photo-realistic 3D scene assets by automatically recognizing, matching, and swapping their materials. Our method proposes a material matching pipeline for the efficient replacement of unknown materials with perceptually similar PBR materials from a database, enabling the quick creation of many variations of a given 3D synthetic scene. At the heart of this method is a novel material similarity feature that is learnt, in conjunction with optimal lighting conditions, by fine-tuning a deep neural network on a material classification task using our proposed dataset. Our evaluation demonstrates that lighting optimization improves CNN-based texture feature extraction methods and better estimates material properties. We conduct a series of experiments showing our method's ability to augment photo-realistic indoor scenes using both standard and procedurally generated PBR materials.

关键词： Training Three-dimensional displays Pipelines Neural networks Lighting Feature extraction Rendering (computer graphics)

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Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity

Revisiting Vicinal Risk Minimization for Partially Supervise...

引用

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

作者： Dong, Nanqing Wang, Jiayi Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England Univ Oxford Math Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect multiple small partially labeled datasets from different matching sources, where the medical images may have only been annotated for a subset of classes of interest. This paper offers an empirical understanding of an under-explored problem, namely partially supervised multi-label classification (PSMLC), where a multi-label classifier is trained with only partially labeled medical images. In contrast to the fully supervised counterpart, the partial supervision caused by medical data scarcity has non-trivial negative impacts on the model performance. A potential remedy could be augmenting the partial labels. Though vicinal risk minimization (VRM) has been a promising solution to improve the generalization ability of the model, its application to PSMLC remains an open question. To bridge the methodological gap, we provide the first VRM-based solution to PSMLC. The empirical results also provide insights into future research directions on partially supervised learning under data scarcity.

关键词： Bridges computer vision Costs conferences Supervised learning Entropy Data models

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Ex-Model: Continual Learning from a Stream of Trained Models

Ex-Model: Continual Learning from a Stream of Trained Models

引用

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

作者： Carta, Antonio Cossu, Andrea Lomonaco, Vincenzo Bacciu, Davide Univ Pisa Pisa Italy Scuola Normale Super Pisa Pisa Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.

关键词： Learning systems Data privacy computer vision conferences Computational modeling Data models pattern recognition

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CORE: Consistent Representation Learning for Face Forgery Detection

CORE: Consistent Representation Learning for Face Forgery De...

引用

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

作者： Ni, Yunsheng Meng, Depu Yu, Changqian Quan, Chengbin Ren, Dongchun Zhao, Youjian Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Meituan Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://***/niyunsheng/CORE.

关键词： Representation learning computer vision Codes Face recognition conferences Market research Forgery

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

Unsupervised Domain Adaptation for Cardiac Segmentation: Tow...

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

作者： Lu, Changjie Zheng, Shen Gupta, Gaurav Wenzhou Kean Univ Coll Sci & Technol Wenzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a specific domain and between different domains is slight. However, the generalization ability of these models on diverse imaging modalities remains a significant challenge. This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation with a compact loss function lower bound. To estimate this new lower bound, we develop a novel Structure Mutual Information Estimation (SMIE) block with a global estimator, a local estimator, and a prior information matching estimator to maximize the mutual information between the reconstruction and segmentation tasks. Specifically, we design a novel sequential reparameterization scheme that enables information flow and variance correction from the low-resolution latent space to the high-resolution latent space. Comprehensive experiments on benchmark cardiac segmentation datasets demonstrate that our model outperforms previous state-of-the-art qualitatively and quantitatively.

关键词： Image segmentation Adaptation models conferences Estimation Benchmark testing pattern recognition Task analysis

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Semi-Supervised Training to Improve Player and Ball Detection in Soccer

Semi-Supervised Training to Improve Player and Ball Detectio...

引用

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

作者： Vandeghen, Renaud Cioppa, Anthony Van Droogenbroeck, Marc Univ Liege Liege Belgium

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Accurate player and ball detection has become increasingly important in recent years for sport analytics. As most state-of-the-art methods rely on training deep learning networks in a supervised fashion, they require huge amounts of annotated data, which are rarely available. In this paper, we present a novel generic semi-supervised method to train a network based on a labeled image dataset by leveraging a large unlabeled dataset of soccer broadcast videos. More precisely, we design a teacher-student approach in which the teacher produces surrogate annotations on the unlabeled data to be used later for training a student which has the same architecture as the teacher. Furthermore, we introduce three training loss parametrizations that allow the student to doubt the predictions of the teacher during training depending on the proposal confidence score. We show that including unlabeled data in the training process allows to substantially improve the performances of the detection network trained only on the labeled data. Finally, we provide a thorough performance study including different proportions of labeled and unlabeled data, and establish the first benchmark on the new SoccerNet-v3 detection task, with an mAP of 52.3%.

关键词： Training Adaptation models Annotations Benchmark testing Data models pattern recognition Proposals

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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CNLL: A Semi-supervised Approach For Continual Noisy Label Learning

CNLL: A Semi-supervised Approach For Continual Noisy Label L...

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

作者： Karim, Nazmul Khalid, Umar Esmaeili, Ashkan Rahnavard, Nazanin Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few studies have addressed the issue of continual learning under noisy labels, long training time and complicated training schemes limit their applications in most cases. In contrast, we propose a simple purification technique to effectively cleanse the online data stream that is both cost-effective and more accurate. After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples. Training in this fashion helps us learn a better representation that results in state-of-the-art (SOTA) performance. Through extensive experimentation on 3 benchmark datasets, MNIST, CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA methods. Our code is publicly available.(1)

关键词： Training computer vision Codes Purification conferences Benchmark testing Performance gain

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