2019 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献_第2页

CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization CamNet:相机再定位的粗到精检索

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00296

Mingyu Ding, Zhe Wang, Jiankai Sun, Jianping Shi, P. Luo

{"title":"CamNet: Coarse-to-Fine Retrieval for Camera Re-Localization","authors":"Mingyu Ding, Zhe Wang, Jiankai Sun, Jianping Shi, P. Luo","doi":"10.1109/ICCV.2019.00296","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00296","url":null,"abstract":"Camera re-localization is an important but challenging task in applications like robotics and autonomous driving. Recently, retrieval-based methods have been considered as a promising direction as they can be easily generalized to novel scenes. Despite significant progress has been made, we observe that the performance bottleneck of previous methods actually lies in the retrieval module. These methods use the same features for both retrieval and relative pose regression tasks which have potential conflicts in learning. To this end, here we present a coarse-to-fine retrieval-based deep learning framework, which includes three steps, i.e., image-based coarse retrieval, pose-based fine retrieval and precise relative pose regression. With our carefully designed retrieval module, the relative pose regression task can be surprisingly simpler. We design novel retrieval losses with batch hard sampling criterion and two-stage retrieval to locate samples that adapt to the relative pose regression task. Extensive experiments show that our model (CamNet) outperforms the state-of-the-art methods by a large margin on both indoor and outdoor datasets.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"22 1","pages":"2871-2880"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81509505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 101

AWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation AWSD:视频表示的自适应加权时空蒸馏

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00811

M. Tavakolian, H. R. Tavakoli, A. Hadid

{"title":"AWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation","authors":"M. Tavakolian, H. R. Tavakoli, A. Hadid","doi":"10.1109/ICCV.2019.00811","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00811","url":null,"abstract":"We propose an Adaptive Weighted Spatiotemporal Distillation (AWSD) technique for video representation by encoding the appearance and dynamics of the videos into a single RGB image map. This is obtained by adaptively dividing the videos into small segments and comparing two consecutive segments. This allows using pre-trained models on still images for video classification while successfully capturing the spatiotemporal variations in the videos. The adaptive segment selection enables effective encoding of the essential discriminative information of untrimmed videos. Based on Gaussian Scale Mixture, we compute the weights by extracting the mutual information between two consecutive segments. Unlike pooling-based methods, our AWSD gives more importance to the frames that characterize actions or events thanks to its adaptive segment length selection. We conducted extensive experimental analysis to evaluate the effectiveness of our proposed method and compared our results against those of recent state-of-the-art methods on four benchmark datatsets, including UCF101, HMDB51, ActivityNet v1.3, and Maryland. The obtained results on these benchmark datatsets showed that our method significantly outperforms earlier works and sets the new state-of-the-art performance in video classification. Code is available at the project webpage: https://mohammadt68.github.io/AWSD/","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"5 1","pages":"8019-8028"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84308891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation 弱监督语义分割的自监督差分检测

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00531

Wataru Shimoda, Keiji Yanai

{"title":"Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation","authors":"Wataru Shimoda, Keiji Yanai","doi":"10.1109/ICCV.2019.00531","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00531","url":null,"abstract":"To minimize the annotation costs associated with the training of semantic segmentation models, researchers have extensively investigated weakly-supervised segmentation approaches. In the current weakly-supervised segmentation methods, the most widely adopted approach is based on visualization. However, the visualization results are not generally equal to semantic segmentation. Therefore, to perform accurate semantic segmentation under the weakly supervised condition, it is necessary to consider the mapping functions that convert the visualization results into semantic segmentation. For such mapping functions, the conditional random field and iterative re-training using the outputs of a segmentation model are usually used. However, these methods do not always guarantee improvements in accuracy; therefore, if we apply these mapping functions iteratively multiple times, eventually the accuracy will not improve or will decrease. In this paper, to make the most of such mapping functions, we assume that the results of the mapping function include noise, and we improve the accuracy by removing noise. To achieve our aim, we propose the self-supervised difference detection module, which estimates noise from the results of the mapping functions by predicting the difference between the segmentation masks before and after the mapping. We verified the effectiveness of the proposed method by performing experiments on the PASCAL Visual Object Classes 2012 dataset, and we achieved 64.9% in the val set and 65.5% in the test set. Both of the results become new state-of-the-art under the same setting of weakly supervised semantic segmentation.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"20 1","pages":"5207-5216"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84334766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 116

Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning 视频字幕的联合语法表示学习与视觉提示翻译

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00901

Jingyi Hou, Xinxiao Wu, Wentian Zhao, Jiebo Luo, Yunde Jia

{"title":"Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning","authors":"Jingyi Hou, Xinxiao Wu, Wentian Zhao, Jiebo Luo, Yunde Jia","doi":"10.1109/ICCV.2019.00901","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00901","url":null,"abstract":"Video captioning is a challenging task that involves not only visual perception but also syntax representation learning. Recent progress in video captioning has been achieved through visual perception, but syntax representation learning is still under-explored. We propose a novel video captioning approach that takes into account both visual perception and syntax representation learning to generate accurate descriptions of videos. Specifically, we use sentence templates composed of Part-of-Speech (POS) tags to represent the syntax structure of captions, and accordingly, syntax representation learning is performed by directly inferring POS tags from videos. The visual perception is implemented by a mixture model which translates visual cues into lexical words that are conditional on the learned syntactic structure of sentences. Thus, a video captioning task consists of two sub-tasks: video POS tagging and visual cue translation, which are jointly modeled and trained in an end-to-end fashion. Evaluations on three public benchmark datasets demonstrate that our proposed method achieves substantially better performance than the state-of-the-art methods, which validates the superiority of joint modeling of syntax representation learning and visual perception for video captioning.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"29 1","pages":"8917-8926"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84468281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 71

Deep Multi-Model Fusion for Single-Image Dehazing 单幅图像去雾的深度多模型融合

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00254

Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, J. Qin, P. Heng

引用次数: 79

Adversarial Defense via Learning to Generate Diverse Attacks 通过学习产生不同的攻击进行对抗性防御

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00283

Y. Jang, Tianchen Zhao, Seunghoon Hong, Honglak Lee

引用次数: 72

Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval 情感图像检索的注意感知极性敏感嵌入

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00123

Xingxu Yao, Dongyu She, Sicheng Zhao, Jie Liang, Yu-Kun Lai, Jufeng Yang

{"title":"Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval","authors":"Xingxu Yao, Dongyu She, Sicheng Zhao, Jie Liang, Yu-Kun Lai, Jufeng Yang","doi":"10.1109/ICCV.2019.00123","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00123","url":null,"abstract":"Images play a crucial role for people to express their opinions online due to the increasing popularity of social networks. While an affective image retrieval system is useful for obtaining visual contents with desired emotions from a massive repository, the abstract and subjective characteristics make the task challenging. To address the problem, this paper introduces an Attention-aware Polarity Sensitive Embedding (APSE) network to learn affective representations in an end-to-end manner. First, to automatically discover and model the informative regions of interest, we develop a hierarchical attention mechanism, in which both polarity- and emotion-specific attended representations are aggregated for discriminative feature embedding. Second, we present a weighted emotion-pair loss to take the inter- and intra-polarity relationships of the emotional labels into consideration. Guided by attention module, we weight the sample pairs adaptively which further improves the performance of feature embedding. Extensive experiments on four popular benchmark datasets show that the proposed method performs favorably against the state-of-the-art approaches.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"21 1","pages":"1140-1150"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85439336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation 基于先验驱动不确定性近似的普遍对抗性扰动

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00303

Hong Liu, Rongrong Ji, Jie Li, Baochang Zhang, Yue Gao, Yongjian Wu, Feiyue Huang

{"title":"Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation","authors":"Hong Liu, Rongrong Ji, Jie Li, Baochang Zhang, Yue Gao, Yongjian Wu, Feiyue Huang","doi":"10.1109/ICCV.2019.00303","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00303","url":null,"abstract":"Deep learning models have shown their vulnerabilities to universal adversarial perturbations (UAP), which are quasi-imperceptible. Compared to the conventional supervised UAPs that suffer from the knowledge of training data, the data-independent unsupervised UAPs are more applicable. Existing unsupervised methods fail to take advantage of the model uncertainty to produce robust perturbations. In this paper, we propose a new unsupervised universal adversarial perturbation method, termed as Prior Driven Uncertainty Approximation (PD-UA), to generate a robust UAP by fully exploiting the model uncertainty at each network layer. Specifically, a Monte Carlo sampling method is deployed to activate more neurons to increase the model uncertainty for a better adversarial perturbation. Thereafter, a textural bias prior to revealing a statistical uncertainty is proposed, which helps to improve the attacking performance. The UAP is crafted by the stochastic gradient descent algorithm with a boosted momentum optimizer, and a Laplacian pyramid frequency model is finally used to maintain the statistical uncertainty. Extensive experiments demonstrate that our method achieves well attacking performances on the ImageNet validation set, and significantly improves the fooling rate compared with the state-of-the-art methods.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"58 1","pages":"2941-2949"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84029013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations 基于超像素关系的动态场景单目分段深度估计

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00446

D. Yan, Henrique Morimitsu, Shan Gao, Xiangyang Ji

{"title":"Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations","authors":"D. Yan, Henrique Morimitsu, Shan Gao, Xiangyang Ji","doi":"10.1109/ICCV.2019.00446","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00446","url":null,"abstract":"In this paper, we propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relative scale ambiguity (RSA) problem and smooth the depth map. However, directly estimating spatial relations is an ill-posed problem. Our core idea is to predict spatial relations based on the corresponding motion relations. Given two or more consecutive frames, we first compute semi-dense (CPM) or dense (optical flow) point matches between temporally neighboring images. Then we develop our method in four main stages: superpixel relations analysis, motion selection, reconstruction, and refinement. The final refinement process helps to improve the quality of the reconstruction at pixel level. Our method does not require per-object segmentation, template priors or training sets, which ensures flexibility in various applications. Extensive experiments on both synthetic and real datasets demonstrate that our method robustly handles different dynamic situations and presents competitive results to the state-of-the-art methods while running much faster than them.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"76 1","pages":"4362-4371"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84034195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Order-Aware Generative Modeling Using the 3D-Craft Dataset 使用3D-Craft数据集的顺序感知生成建模

2019 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2019-10-01 DOI: 10.1109/ICCV.2019.00185

Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, C. Qi, Shubham Tulsiani, Arthur Szlam, C. L. Zitnick

{"title":"Order-Aware Generative Modeling Using the 3D-Craft Dataset","authors":"Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, C. Qi, Shubham Tulsiani, Arthur Szlam, C. L. Zitnick","doi":"10.1109/ICCV.2019.00185","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00185","url":null,"abstract":"In this paper, we study the problem of sequentially building houses in the game of Minecraft, and demonstrate that learning the ordering can make for more effective autoregressive models. Given a partially built house made by a human player, our system tries to place additional blocks in a human-like manner to complete the house. We introduce a new dataset, HouseCraft, for this new task. HouseCraft contains the sequential order in which 2,500 Minecraft houses were built from scratch by humans. The human action sequences enable us to learn an order-aware generative model called Voxel-CNN. In contrast to many generative models where the sequential generation ordering either does not matter (e.g. holistic generation with GANs), or is manually/arbitrarily set by simple rules (e.g. raster-scan order), our focus is on an ordered generation that imitates humans. To evaluate if a generative model can accurately predict human-like actions, we propose several novel quantitative metrics. We demonstrate that our Voxel-CNN model is simple and effective at this creative task, and can serve as a strong baseline for future research in this direction. The HouseCraft dataset and code with baseline models will be made publicly available.","PeriodicalId":6728,"journal":{"name":"2019 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"6 1","pages":"1764-1773"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84037407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4