Search Results - 2018 IEEE/CVF Conference on Computer Vision AND Pattern Recognition

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) : Salt Lake City, Utah, USA 18-22 June 2018 by IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers

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Deformable Siamese Attention Networks for Visual Object Tracking by Yu, Yuechen, Xiong, Yilei, Huang, Weilin, Scott, Matthew R.

“…Siamese-based trackers have achieved excellent performance on visual object tracking…”
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Two-stream lightweight sign language transformer by Chen, Yuming, Mei, Xue, Qin, Xuan

“…Despite the recent progress of continuous sign language translation-based video, a variety of deep learning models are difficult to apply to the real-time…”
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Deformable GANs for Pose-Based Human Image Generation by Siarohin, Aliaksandr, Sangineto, Enver, Lathuiliere, Stephane, Sebe, Nicu

“…In this paper we address the problem of generating person images conditioned on a given pose…”
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VizWiz Grand Challenge: Answering Visual Questions from Blind People by Gurari, Danna, Li, Qing, Stangl, Abigale J., Guo, Anhong, Lin, Chi, Grauman, Kristen, Luo, Jiebo, Bigham, Jeffrey P.

“…The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial…”
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On the Duality Between Retinex and Image Dehazing by Galdran, Adrian, Bria, Alessandro, Alvarez-Gila, Aitor, Vazquez-Corral, Javier, Bertalmio, Marcelo

“… Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks…”
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Subspace-PnP: A Geometric Constraint Loss for Mutual Assistance of Depth and Optical Flow Estimation by Chi, Cheng, Hao, Tianyu, Wang, Qingjie, Guo, Peng, Yang, Xin

“…, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4654–4665, 2020; Ranjan et al., in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12240…”
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Real-Time Seamless Single Shot 6D Object Pose Prediction by Tekin, Bugra, Sinha, Sudipta N., Fua, Pascal

“… As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing…”
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Single-Shot Refinement Neural Network for Object Detection by Zhang, Shifeng, Wen, Longyin, Bian, Xiao, Lei, Zhen, Li, Stan Z.

“…For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the…”
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DensePose: Dense Human Pose Estimation in the Wild by Guler, Riza Alp, Neverova, Natalia, Kokkinos, Iasonas

“… We further improve accuracy through cascading, obtaining a system that delivers highly-accurate results at multiple frames per second on a single gpu…”
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Features for Multi-target Multi-camera Tracking and Re-identification by Ristani, Ergys, Tomasi, Carlo

“… Our method outperforms the state of the art both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and DukeMTMC-ReID benchmarks for Re-ID…”
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DenseASPP for Semantic Segmentation in Street Scenes by Yang, Maoke, Yu, Kun, Zhang, Chi, Li, Zhiwei, Yang, Kuiyuan

“…Semantic image segmentation is a basic street scene understanding task in autonomous driving, where each pixel in a high resolution image is categorized into a…”
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Mask-Guided Contrastive Attention Model for Person Re-identification by Song, Chunfeng, Huang, Yan, Ouyang, Wanli, Wang, Liang

“…Person Re-identification (ReID) is an important yet challenging task in computer vision…”
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NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images by Arad, Boaz, Liu, Dong, Wu, Feng, Lanaras, Charis, Galliani, Silvano, Schindler, Konrad, Stiebel, Tarek, Koppers, Simon, Seltsam, Philipp, Zhou, Ruofan, El Helou, Majed, Ben-Shahar, Ohad, Lahoud, Fayez, Shahpaski, Marjan, Zheng, Ke, Gao, Lianru, Zhang, Bing, Cui, Ximin, Yu, Haoyang, Can, Yigit Baran, Alvarez-Gila, Aitor, van de Weijer, Joost, Timofte, Radu, Garrote, Estibaliz, Galdran, Adrian, Sharma, Manoj, Koundinya, Sriharsha, Upadhyay, Avinash, Manekar, Raunak, Mukhopadhyay, Rudrabha, Sharma, Himanshu, Chaudhury, Santanu, Nagasubramanian, Koushik, Van Gool, Luc, Ghosal, Sambuddha, Singh, Asheesh K., Singh, Arti, Ganapathysubramanian, Baskar, Sarkar, Soumik, Zhang, Lei, Yang, Ming-Hsuan, Xiong, Zhiwei, Chen, Chang, Shi, Zhan

“…This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e…”
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Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination by Doughty, Hazel, Damen, Dima, Mayol-Cuevas, Walterio

“…This paper presents a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We…”
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Unsupervised Cross-Dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns by Lv, Jianming, Chen, Weihang, Li, Qing, Yang, Can

“…Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale…”
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NestedNet: Learning Nested Sparse Structures in Deep Neural Networks by Kim, Eunwoo, Ahn, Chanho, Oh, Songhwai

“… While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources…”
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CVPR 2019: Call for Papers Deadline November 16: The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Improving Calibration for Long-Tailed Recognition by Zhong, Zhisheng, Cui, Jiequan, Liu, Shu, Jia, Jiaya

“…Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and…”
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Disentangling Label Distribution for Long-tailed Visual Recognition by Hong, Youngkyu, Han, Seungju, Choi, Kwanghee, Seo, Seokjun, Kim, Beomsu, Chang, Buru

“…The current evaluation protocol of long-tailed visual recognition trains the classification model on the long-tailed source label distribution and evaluates its performance on the uniform target label distribution…”
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