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Motion and Depth Augmented Semantic Segmentation for Autonomous Navigation

Motion and Depth Augmented Semantic Segmentation for Autonom...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hazem Rashed Ahmad El Sallab Senthil Yogamani Mohamed ElHelw Valeo R&D Cairo Egypt Valeo Vision Systems Nile University Cairo Egypt

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Motion and depth provide critical information in autonomous driving and they are commonly used for generic object detection. In this paper, we leverage them for improving semantic segmentation. Depth cues can be useful for detecting road as it lies below the horizon line. There is also a strong structural similarity for different instances of different objects including buildings and trees. Motion cues are useful as the scene is highly dynamic with moving objects including vehicles and pedestrians. This work utilizes geometric information modelled by depth maps and motion cues represented by optical flow vectors to improve the pixel-wise segmentation task. A CNN architecture is proposed and the variations regarding the stage at which color, depth, and motion information are fused, e.g. early-fusion or mid-fusion, are systematically investigated. Additionally, we implement a multimodal fusion algorithm to maximize the benefit from all the information. The proposed algorithms are evaluated on Virtual-KITTI and Cityscapes datasets where results demonstrate enhanced performance with depth and flow augmentation.

关键词： Semantics computer architecture Optical imaging Feature extraction Motion segmentation Optical sensors Estimation

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Gabor features in image analysis

Gabor features in image analysis

引用

workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Joni-Kristian Kamarainen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Finland

ISBN: (纸本)9781467325851

In applications of computer vision and image analysis, Gabor filters have maintained their popularity in feature extraction for almost three decades. The original reason that draw attention was the similarity between Gabor filters and the receptive field of simple cells in the visual cortex. A more practical reason is their success in many applications, e.g., face detection and recognition, iris recognition and fingerprint matching, where Gabor feature based methods are among the top performers. The derivation of Gabor features is elegant through the fundamental domains of signal processing: space (time) and frequency. Topped with many practical and computational advantages we will see their use also in future applications.

关键词： Feature extraction Frequency-domain analysis Face recognition Iris recognition computer vision Visualization

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Towards Good Practices for Video Object Segmentation

Towards Good Practices for Video Object Segmentation

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International conference on computer vision workshops (ICCV workshops)

作者： Dongdong Yu Kai Su Hengkai Guo Jian Wang Kaihui Zhou Yuanyuan Huang Minghui Dong Jie Shao Changhu Wang Jie Shao and Changhu Wang ByteDance AI Lab Beijing China ByteDance AI Lab. Beijing China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all the refinements, we improve the space-time memory networks to achieve a Overall of 79.1 on the Youtube-VOS Challenge 2019.

关键词： Object segmentation Training Testing computer vision conferences pattern recognition Neural networks

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Learning Tracking Representations from Single Point Annotations

Learning Tracking Representations from Single Point Annotati...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Qiangqiang Wu Antoni B. Chan Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing deep trackers are typically trained with large-scale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5 × faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.

关键词： Training Visualization Target tracking Costs Correlation Annotations Noise

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Are two rotational flows sufficient to calibrate a smooth non-parametric sensor?

Are two rotational flows sufficient to calibrate a smooth no...

引用

conference on computer vision and pattern recognition (CVPR)

作者： E. Grossmann Eun-Joo Lee P. Hislop D. Nister H. Stewenius Department IRO Universite de Montréal Canada Computer Science Department University of Kentucky USA Department of Mathematics University of Kentucky USA Center for Visualization University of Kentucky USA

We present an attempt to determine whether the shape of a generic central-projection camera, such as the eye of an insect or a log-polar camera, can be determined from two motion flows resulting from purely rotational motions with non-collinear axes. Our first contribution is to write the smooth non-parametric calibration problem as a differential equation. It is unclear at present whether this problem has unique solution, up to an orthogonal transformation. Our second contribution is a discretized version of this smooth problem, for which we give a calibration algorithm - a third contribution. Using this algorithm, we explore numerically the properties of the discrete self-calibration problem, giving some insight on the nature of the problem. We show examples of successful self-calibration, but cannot give a definite affirmative answer to the question in the title.

关键词： Cameras Calibration Optical sensors Visualization computer vision Sensor phenomena and characterization computer science Mathematics Shape Insects

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Tracking and Counting Apples in Orchards Under Intermittent Occlusions and Low Frame Rates

Tracking and Counting Apples in Orchards Under Intermittent ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gonçalo P. Matos Carlos Santiago João P. Costeira Ricardo L. Saldanha Ernesto M. Morgado SISCOG - Sistemas Congitivos SA Lisbon Portugal Institute for Systems and Robotics (ISR/IST) LARSyS Instituto Superior Técnico Univ. of Lisbon Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating what will be the fruit yield in an orchard helps farmers to better plan the resources needed for harvesting, storing, and commercialising the crop, and also to take some agricultural decisions (like pruning) that may increase the quality of the yield and increase profits. Therefore, over the last years, several methods based on computer vision were proposed to automate this task, by directly counting the fruits on trees using a video camera. However, existing works and methods usually assume ideal conditions, and may fail under more challenging scenarios with unconstrained camera motion and intermittent occlusions of fruits. Here we show that combining Structure-from-Motion (SfM) with a bipartite graph matching has the potential to address those challenges. We found that our approach applied to real-world datasets, with unconstrained camera motion and low frame rates, outperforms existing methods by a large margin. Our results demonstrate that the proposed method is robust to multiple intermittent occlusions under challenging conditions, and thus suitable to be used in diverse real-world scenarios in orchards, either with a camera operated by hand or mounted on an agricultural vehicle. Although not shown here, we believe that the proposed method can also be applied to other object tracking problems besides counting fruits, under similar settings — i.e. static objects and a freely moving camera.

关键词： computer vision Visualization conferences Crops Cameras Trajectory pattern recognition

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Learning A Meta-Ensemble Technique For Skin Lesion Classification And Novel Class Detection

Learning A Meta-Ensemble Technique For Skin Lesion Classific...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Subhranil Bagchi Anurag Banerjee Deepti R. Bathula Department of Computer Science and Engineering Indian Institute of Technology Ropar India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The frequency and fatality rates associated with skin Melanoma requires an accurate and efficient detection methodology to enable early medical diagnosis. Artificial Intelligence (AI) augmented detection methods aim at achieving this goal while reducing the costs and time involved in traditional methods. This work utilizes a two-level ensemble learning technique (trained with weighted losses) to improve accuracy over individual classification models. The ensemble technique alleviates over-fitting due to class imbalance in the dataset, achieving a Balanced Multi-class Accuracy (BMA) score of 0.591 without unknown class detection. The algorithm was extended by appending the proposed CS-KSU module collection to detect the presence of images belonging to novel classes during test time. The extended algorithm secured an Area Under the ROC Curve (AUC) score of 0.544 for the unknown class. Our algorithm's performance is at par with the current state-of-the-art for this task 1 .

关键词： Stacking Training Skin Task analysis Lesions Image color analysis Melanoma

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Demographic Bias Effects on Face Image Synthesis

Demographic Bias Effects on Face Image Synthesis

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Roberto Leyva Victor Sanchez Gregory Epiphaniou Carsten Maple WMG University of Warwick UK Computer Science University of Warwick UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face image synthesis has shown remarkable progress in recent years. However, the effect that the demographics of the data used to train synthesizers has on the generation of new face images remains an open question. This paper investigates the effects of the training set demographics in the face image synthesis task. To this end, we propose a strategy that allows synthesizing face images for specific groups of people with a high visual quality. The strategy uses an unsupervised learning approach to discover groups of people in the training set based on Bayesian inference via a probabilistic mixture model. If labels are available to define the groups, our strategy can also exploit such information in lieu of unsupervised learning. Once the groups are defined, our strategy trains a Generative Adversarial Network on each group to generate new face images with specific characteristics. Our results show remarkable performance in terms of image quality compared to several state-of-the-art baselines. More importantly, our strategy allows synthesizing face images with reduced demographic biases.

关键词： Training Image quality Visualization Image synthesis Face recognition Synthesizers Mixture models

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Comparison of Methods for Hyperspherical Data Averaging and Parameter Estimation

Comparison of Methods for Hyperspherical Data Averaging and ...

引用

International conference on pattern recognition

作者： Kai Rothaus Xiaoyi Jiang M. Lambers Department of Computer Science University of Münster Germany

ISBN: (纸本)0769525210

Averaging is an important concept which has found numerous applications in general and in pattern recognition and computer vision in particular. In this paper we consider averaging directional vectors of arbitrary dimensions. Given a set of vectors, we intend to compute an average vector which optimally represents the input vectors according to some formal criterion. Several optimisation criteria are formulated. In particular, we present a class of robust estimators of up to 50% outlier tolerance. Furthermore, we propose a technique to estimate another distribution parameter. Experimental results on spherical data are presented to demonstrate the usefulness of the proposed methods

关键词： Parameter estimation Application software pattern recognition computer vision Shape Surface treatment Quaternions computer science Robustness Data engineering

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Taehoon Kim Mark Marsden Pyunghwan Ahn Sangyun Kim Sihaeng Lee Alessandra Sala Seung Hwan Kim LG AI Research Shutterstock

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： Training Measurement Accuracy conferences computer architecture Feature extraction Data models

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