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检索条件"任意字段=32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019"

共 858 条记录，以下是101-110 订阅

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Rare Event Detection using Disentangled Representation Learning 32

Rare Event Detection using Disentangled Representation Learn...

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

作者： Hamaguchi, Ryuhei Sakurada, Ken Nakamura, Ryosuke Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan

ISBN: (纸本)9781728132938

This paper presents a novel method for rare event detection from an image pair with class-imbalanced datasets. A straightforward approach for event detection tasks is to train a detection network from a large-scale dataset in an end-to-end manner. However, in many applications such as building change detection on satellite images, few positive samples are available for the training. Moreover, scene image pairs contain many trivial events, such as in illumination changes or background motions. These many trivial events and the class imbalance problem lead to false alarms for rare event detection. In order to overcome these difficulties, we propose a novel method to learn disentangled representations from only low-cost negative samples. The proposed method disentangles different aspects in a pair of observations: variant and invariant factors that represent trivial events and image contents, respectively. The effectiveness of the proposed approach is verified by the quantitative evaluations on four change detection datasets, and the qualitative analysis shows that the proposed method can acquire the representations that disentangle rare events from trivial ones.

关键词： Categorization Deep Learning recognition: Detection Representation Learning Retrieval Scene Analysis and Understanding vision Applications and S

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Informative Object Annotations: Tell Me Something I Don't Know 32

Informative Object Annotations: Tell Me Something I Don't Kn...

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

作者： Bracha, Lior Chechik, Gal Bar Ilan Univ Ramat Gan Israel Bar Ilan Univ NVIDIA Res Ramat Gan Israel

ISBN: (纸本)9781728132938

Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and use this uncertainty reduction to rank candidate labels for describing an image. While the full estimation problem is intractable, we describe an efficient algorithm to approximate entropy reduction using a tree-structured graphical model. We evaluate our approach on the open-images dataset using a new evaluation set of 10K ground-truth ratings and find that it achieves similar to 65% agreement with human raters, close to the upper bound of inter-rater agreement and largely outperforming other unsupervised baselines.

关键词： Categorization recognition: Detection Retrieval Scene Analysis and Understanding Statistical Lea vision + Language

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Reflection Removal Using a Dual-Pixel Sensor 32

Reflection Removal Using a Dual-Pixel Sensor

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

作者： Punnappurath, Abhijith Brown, Michael S. York Univ N York ON Canada

ISBN: (纸本)9781728132938

Reflection removal is the challenging problem of removing unwanted reflections that occur when imaging a scene that is behind a pane of glass. In this paper, we show that most cameras have an overlooked mechanism that can greatly simplify this task. Specifically, modern DLSR and smartphone cameras use dual pixel (DP) sensors that have two photodiodes per pixel to provide two sub-aperture views of the scene from a single captured image. "Defocus-disparity" cues, which are natural by-products of the DP sensor encoded within these two sub-aperture views, can be used to distinguish between image gradients belonging to the in focus background and those caused by reflection interference. This gradient information can then be incorporated into an optimization framework to recover the background layer with higher accuracy than currently possible from the single captured image. As part of this work, we provide the first image dataset for reflection removal consisting of the sub-aperture views from the DP sensor.

关键词： Computational Photography Low-level vision

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Activity Driven Weakly Supervised Object Detection 32

Activity Driven Weakly Supervised Object Detection

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

作者： Yang, Zhenheng Mahajan, Dhruv Ghadiyaram, Deepti Nevatia, Ram Ramanathan, Vignesh Univ Southern Calif Los Angeles CA 90007 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781728132938

Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled only with the object class and not the object bounding box. In our work, we try to leverage not only the object class labels but also the action labels associated with the data. We show that the action depicted in the image/video can provide strong cues about the location of the associated object. We learn a spatial prior for the object dependent on the action (e.g. "ball" is closer to "leg of the person" in "kicking ball"), and incorporate this prior to simultaneously train a joint object detection and action classification model. We conducted experiments on both video datasets and image datasets to evaluate the performance of our weakly supervised object detection model. Our approach outperformed the current state-of-the-art (SOTA) method by more than 6% in mAP on the Charades video dataset.

关键词： Action recognition Categorization recognition: Detection Retrieval Video Analytics

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Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation 32

Art2Real: Unfolding the Reality of Artworks via Semantically...

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

作者： Tomei, Matteo Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781728132938

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code is publicly available at: https://***/aimagelab/art2real.

关键词： Deep Learning Image and Video Synthesis vision Applications and Systems

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REPAIR: Removing Representation Bias by Dataset Resampling 32

REPAIR: Removing Representation Bias by Dataset Resampling

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

作者： Li, Yi Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728132938

Modern machine learning datasets can have biases for certain representations that are leveraged by algorithms to achieve high peiformance without learning to solve the underlying task. This problem is referred to as "representation bias". The question of how to reduce the representation biases of a dataset is investigated and a new dataset REPresentAtion bIas Removal (REPAIR) procedure is proposed. This formulates bias minimization as an optimization problem, seeking a weight distribution that penalizes examples easy for a classifier built on a given feature representation. Bias reduction is then equated to maximizing the ratio between the classification loss on the reweighted dataset and the uncertainty of the ground-truth class labels. This is a minimax problem that REPAIR solves by alternatingly updating classifier parameters and dataset re sampling weights, using stochastic gradient descent. An experimental set-up is also introduced to measure the bias of any dataset for a given representation, and the impact of this bias on the peiformance of recognition models. Experiments with synthetic and action recognition data show that dataset REPAIR can significantly reduce representation bias, and lead to improved generalization of models trained on REPAIRed datasets.

关键词： Action recognition Datasets and Evaluation Deep Learning Representation Learning Video Analytics

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Actively Seeking and Learning from Live Data 32

Actively Seeking and Learning from Live Data

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

作者： Teney, Damien van den Hengel, Anton Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia

ISBN: (纸本)9781728132938

One of the key limitations of traditional machine learning methods is their requirement for training data that exemplifies all the information to be learned. This is a particular problem for visual question answering methods, which may be asked questions about virtually anything. The approach we propose is a step toward overcoming this limitation by searching for the information required at test time. The resulting method dynamically utilizes data from an external source, such as a large set of questions/answers or images/captions. Concretely, we learn a set of base weights for a simple VQA model, that are specifically adapted to a given question with the information specifically retrieved for this question. The adaptation process leverages recent advances in gradient-based meta learning and contributions for efficient retrieval and cross-domain adaptation. We surpass the state-of-the-art on the VQA-CP v2 benchmark and demonstrate our approach to be intrinsically more robust to out-of-distribution test data. We demonstrate the use of external non-VQA data using the MS COCO captioning dataset to support the answering process. This approach opens a new avenue for open-domain VQA systems that interface with diverse sources of data.

关键词： vision + Language Visual Reasoning

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Less is More: Learning Highlight Detection from Video Duration 32

Less is More: Learning Highlight Detection from Video Durati...

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

作者： Xiong, Bo Kalantidis, Yannis Ghadiyaram, Deepti Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Facebook AI Menlo Pk CA USA Facebook AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9781728132938

Highlight detection has the potential to significantly ease video browsing, but existing methods often suffer from expensive supervision requirements, where human viewers must manually identify highlights in training videos. We propose a scalable unsupervised solution that exploits video duration as an implicit supervision signal. Our key insight is that video segments from shorter user-generated videos are more likely to be highlights than those from longer videos, since users tend to be more selective about the content when capturing shorter videos. Leveraging this insight, we introduce a novel ranking framework that prefers segments from shorter videos, while properly accounting for the inherent noise in the (unlabeled) training data. We use it to train a highlight detector with 10M hashtagged Instagram videos. In experiments on two challenging public video highlight detection benchmarks, our method substantially improves the state-of-the-art for unsupervised highlight detection.

关键词： Action recognition Categorization recognition: Detection Retrieval Video Analytics

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Image-Question-Answer Synergistic Network for Visual Dialog 32

Image-Question-Answer Synergistic Network for Visual Dialog

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

作者： Guo, Dalu Xu, Chang Tao, Dacheng Univ Sydney UBTECH Sydney AI Ctr Sch Comp Sci FEIT Darlington NSW 2008 Australia

ISBN: (纸本)9781728132938

The image, question (combined with the history for dereferencing), and the corresponding answer are three vital components of visual dialog. Classical visual dialog systems integrate the image, question, and history to search for or generate the best matched answer, and so, this approach significantly ignores the role of the answer. In this paper, we devise a novel image-question-answer synergistic network to value the role of the answer for precise visual dialog. We extend the traditional one-stage solution to a two-stage solution. In the first stage, candidate answers are coarsely scored according to their relevance to the image and question pair. Afterward, in the second stage, answers with high probability of being correct are re-ranked by synergizing with image and question. On the Visual Dialog v1.0 dataset, the proposed synergistic network boosts the discriminative visual dialog model to achieve a new state-of-the-art of 57.88% normalized discounted cumulative gain. A generative visual dialog model equipped with the proposed technique also shows promising improvements.

关键词： Deep Learning vision + Language

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Pedestrian Detection with Autoregressive Network Phases 32

Pedestrian Detection with Autoregressive Network Phases

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

作者： Brazil, Garrick Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781728132938

We present an autoregressive pedestrian detection framework with cascaded phases designed to progressively improve precision. The proposed framework utilizes a novel lightweight stackable decoder-encoder module which uses convolutional re-sampling layers to improve features while maintaining efficient memory and runtime cost. Unlike previous cascaded detection systems, our proposed framework is designed within a region proposal network and thus retains greater context of nearby detections compared to independently processed RoI systems. We explicitly encourage increasing levels of precision by assigning strict labeling policies to each consecutive phase such that early phases develop features primarily focused on achieving high recall and later on accurate precision. In consequence, the final feature maps form more peaky radial gradients emulating from the centroids of unique pedestrians. Using our proposed autoregressive framework leads to new state-of-the-art performance on the reasonable and occlusion settings of the Caltech pedestrian dataset, and achieves competitive state-of-the-art performance on the KITTI dataset.

关键词： Categorization recognition: Detection Retrieval Robotics + Driving Scene Analysis and Understanding vision Applications and Systems

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