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Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos

Spatial-Temporal Correlation and Topology Learning for Perso...

引用

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

作者： Liu, Jiawei Zha, Zheng-Jun Wu, Wei Zheng, Kecheng Sun, Qibin Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665445092

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re- identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. Specifically, CTL utilizes a CNN backbone and a key points estimator to extract semantic local features from human body at multiple granularities as graph nodes. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body. Moreover, a 3D graph convolution and a cross-scale graph convolution are designed, which facilitate direct cross-spacetime and cross-scale information propagation for capturing hierarchical spatial-temporal dependencies and structural information. By jointly performing the two convolutions, CTL effectively mines comprehensive clues that are complementary with appearance information to enhance representational capacity. Extensive experiments on two video benchmarks have demonstrated the effectiveness of the proposed method and the state-of-the-art performance.

关键词： computer vision Correlation Three-dimensional displays Convolution Biological system modeling Video sequences Semantics

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Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset ...

引用

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

作者： Haurum, Joakim Bruslund Moeslund, Thomas B. Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark

ISBN: (纸本)9781665445092

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1Normal. The benchmark algorithm achieves an F2CIW score of 55.11% and F1Normal score of 90.94%, leaving ample room for improvement on the SewerML dataset. The code, models, and dataset are available at the project page http://***/sewer-ml

关键词： Measurement Economics computer vision Codes Biological system modeling Benchmark testing Inspection

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Multi-shot Temporal Event Localization: a Benchmark

Multi-shot Temporal Event Localization: a Benchmark

引用

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

作者： Liu, Xiaolong Hu, Yao Bai, Song Ding, Fei Bai, Xiang Torr, Philip H. S. Huazhong Univ Sci & Technol Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot temporal event localization, and accordingly, collect a large-scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event instances for a total of 716 video hours. The core nature of MUSES is the frequent shot cuts, for an average of 19 shots per instance and 176 shots per video, which induces large intra-instance variations. Our comprehensive evaluations show that the state-of-the-art method in temporal action localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution, we present a simple baseline approach for handling the intra-instance variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at IoU=0.5. To facilitate research in this direction, we release the dataset and the project code at https://***/muses/.

关键词： Location awareness computer vision TV Codes Annotations Benchmark testing Motion pictures

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Probabilistic Selective Encryption of Convolutional Neural Networks for Hierarchical Services

Probabilistic Selective Encryption of Convolutional Neural N...

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

作者： Tian, Jinyu Zhou, Jiantao Duan, Jia Univ Macau State Key Lab Internet Things Smart City Dept Comp & Informat Sci Taipa Macao Peoples R China JD Explore JD Gauteng South Africa

ISBN: (纸本)9781665445092

Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them. In this work, we propose a selective encryption (SE) algorithm to protect CNN models from unauthorized access, with a unique feature of providing hierarchical services to users. Our algorithm firstly selects important model parameters via the proposed Probabilistic Selection Strategy (PSS). It then encrypts the most important parameters with the designed encryption method called Distribution Preserving Random Mask (DPRM), so as to maximize the performance degradation by encrypting only a very small portion of model parameters. We also design a set of access permissions, using which different amount of most important model parameters can be decrypted. Hence, different levels of model performance can be naturally provided for users. Experimental results demonstrate that the proposed scheme could effectively protect the classification model VGG19 by merely encrypting 8% parameters of convolutional layers. We also implement the proposed model protection scheme in the denoising model DnCNN, showcasing the hierarchical denoising services.

关键词： Training Degradation computer vision Costs Computational modeling Noise reduction Probabilistic logic

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End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving

End-to-End Interactive Prediction and Planning with Optical ...

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

作者： Wang, Hengli Cai, Peide Fan, Rui Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Calif San Diego La Jolla CA 92093 USA Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665448994

With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, most of these approaches follow a non-interactive prediction and planning paradigm, hypothesizing that a vehicle's behaviors do not affect others. The approaches based on such a non-interactive philosophy typically perform acceptably in sparse traffic scenarios but can easily fail in dense traffic scenarios. Therefore, we propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Given a set of past surrounding-view images and a high definition map, our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed. Extensive experiments on the nuScenes dataset and in the closed-loop Carla simulation environment demonstrate the effectiveness and efficiency of our INMP for the detection, prediction, and planning tasks. Our project page is at ***/view/inmp-ofd.

关键词： Philosophical considerations Predictive models Feature extraction Real-time systems Planning pattern recognition Task analysis

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StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis

StEP: Style-based Encoder Pre-training for Multi-modal Image...

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

作者： Meshry, Moustafa Ren, Yixuan Davis, Larry S. Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665445092

We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex training objectives to learn a latent embedding, jointly with the generator, that models the variability of the output domain. In contrast, we directly model the style variability of images, independent of the image synthesis task. Specifically, we pre-train a generic style encoder using a novel proxy task to learn an embedding of images, from arbitrary domains, into a low-dimensional style latent space. The learned latent space introduces several advantages over previous traditional approaches to multi-modal I2I translation. First, it is not dependent on the target dataset, and generalizes well across multiple domains. Second, it learns a more powerful and expressive latent space, which improves the fidelity of style capture and transfer. The proposed style pre-training also simplifies the training objective and speeds up the training significantly. Furthermore, we provide a detailed study of the contribution of different loss terms to the task of multi-modal I2I translation, and propose a simple alternative to VAEs to enable sampling from unconstrained latent spaces. Finally, we achieve state-of-the-art results on six challenging benchmarks with a simple training objective that includes only a GAN loss and a reconstruction loss.

关键词： Training computer vision Codes Image synthesis Benchmark testing Generative adversarial networks Generators

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PML: Progressive Margin Loss for Long-tailed Age Classification

PML: Progressive Margin Loss for Long-tailed Age Classificat...

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

作者： Deng, Zongyong Liu, Hao Wang, Yaoxing Wang, Chenyang Yu, Zekuan Sun, Xuehong Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Municipal & Minist Educ Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification. Conventional methods make strong assumption on that each class owns adequate instances to outline its data distribution, likely leading to bias prediction where the training samples are sparse across age classes. Instead, our PML aims to adaptively refine the age label pattern by enforcing a couple of margins, which fully takes in the in-between discrepancy of the intra-class variance, inter-class variance and class center. Our PML typically incorporates with the ordinal margin and the variational margin, simultaneously plugging in the globally-tuned deep neural network paradigm. More specifically, the ordinal margin learns to exploit the correlated relationship of the real-world age labels. Accordingly, the variational margin is leveraged to minimize the influence of head classes that misleads the prediction of tailed samples. Moreover, our optimization carefully seeks a series of indicator curricula to achieve robust and efficient model training. Extensive experimental results on three face aging datasets demonstrate that our PML achieves compelling performance compared to state of the art. Code will be made publicly.

关键词： Training Deep learning computer vision Codes Face recognition Aging Optimization

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Bi-GCN: Binary Graph Convolutional Network

Bi-GCN: Binary Graph Convolutional Network

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

作者： Wang, Junfu Wang, Yunhong Yang, Zhen Yang, Liang Guo, Yuanfang Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781665445092

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to propose a Binary Graph Convolutional Network (Bi-GCN), which binarizes both the network parameters and input node features. Besides, the original matrix multiplications are revised to binary operations for accelerations. According to the theoretical analysis, our Bi-GCN can reduce the memory consumption by an average of similar to 30x for both the network parameters and input data, and accelerate the inference speed by an average of similar to 47x, on the citation networks. Meanwhile, we also design a new gradient approximation based back-propagation method to train our Bi-GCN well. Extensive experiments have demonstrated that our Bi-GCN can give a comparable performance compared to the full-precision baselines. Besides, our binarization approach can be easily applied to other GNNs, which has been verified in the experiments.

关键词： computer vision Memory management Loading Graph neural networks pattern recognition Task analysis

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Roses are Red, Violets are Blue... But Should VQA expect Them To?

Roses are Red, Violets are Blue... But Should VQA expect The...

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

作者： Kervadec, Corentin Antipov, Grigory Baccouche, Moez Wolf, Christian Cesson Seyigne Orange France INSA Lyon LIRIS UMR CNRS 5205 Lyon France

ISBN: (纸本)9781665445092

Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to "reason", leading them to perform "educated guesses" instead. In this paper, we claim that the standard evaluation metric, which consists in measuring the overall in-domain accuracy, is misleading. Since questions and concepts are unbalanced, this tends to favor models which exploit subtle training set statistics. Alternatively, naively introducing artificial distribution shifts between train and test splits is also not completely satisfying. First, the shifts do not reflect real-world tendencies, resulting in unsuitable models;second, since the shifts are handcrafted, trained models are specifically designed for this particular setting, and do not generalize to other configurations. We propose the GQAOOD benchmark designed to overcome these concerns: we measure and compare accuracy over both rare and frequent question-answer pairs, and argue that the former is better suited to the evaluation of reasoning abilities, which we experimentally validate with models trained to more or less exploit biases. In a large-scale study involving 7 VQA models and 3 bias reduction techniques, we also experimentally demonstrate that these models fail to address questions involving infrequent concepts and provide recommendations for future directions of research.

关键词： Training Measurement Visualization computer vision Computational modeling Benchmark testing Knowledge discovery

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All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

All Labels Are Not Created Equal: Enhancing Semi-supervision...

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

作者： Nassar, Islam Herath, Samitha Abbasnejad, Ehsan Buntine, Wray Haffari, Gholamreza Monash Univ Dept Data Sci & AI Fac IT Clayton Vic Australia Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9781665445092

Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against. A common property among its various methods is that they only rely on the model's prediction to make labeling decisions without considering any prior knowledge about the visual similarity among the classes. In this paper, we demonstrate that this degrades the quality of pseudo-labeling as it poorly represents visually similar classes in the pool of pseudo-labeled data. We propose SemCo, a method which leverages label semantics and co-training to address this problem. We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together. We then co-train the two classifiers to learn based on their disagreements. We show that our method achieves state-of-the-art performance across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet dataset with 1000 labeled examples. We also show that our method requires smaller batch size and fewer training iterations to reach its best performance.

关键词： Training Visualization Semantics Semisupervised learning Predictive models pattern recognition Labeling

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