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I Know How You Feel: Emotion recognition with Facial Landmarks 31

I Know How You Feel: Emotion Recognition with Facial Landmar...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tautkute, Ivona Trzcinski, Tomasz Bielski, Adam Tooploox Wroclaw Poland Polish Japanese Acad Informat Technol Warsaw Poland Warsaw Univ Technol Warsaw Poland

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multilayered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.

关键词： Emotion recognition Task analysis Face Face recognition Convolutional neural networks Databases

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A Holistic Framework for Addressing the World using Machine Learning 31

A Holistic Framework for Addressing the World using Machine ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Demir, Ilke Hughes, Forest Raj, Aman Dhruv, Kaunil Raskar, Ramesh Muddala, Suryanarayana Murthy Garg, Sanyam Doo, Barrett Facebook Menlo Pk CA 94025 USA MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Millions of people are disconnected from basic services due to lack of adequate addressing. We propose an automatic generative algorithm to create street addresses from satellite imagery. Our addressing scheme is coherent with the street topology, linear and hierarchical to follow human perception, and universal to be used as a unified geocoding system. Our algorithm starts with extracting road segments using deep learning and partitions the road network into regions. Then regions, streets, and address cells are named using proximity computations. We also extend our addressing scheme to cover inaccessible areas, to be flexible for changes, and to lead as a pioneer for a unified geodatabase.

关键词： Roads Urban areas Satellites Image segmentation computer vision conferences Topology

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Cross-domain fashion image retrieval 31

Cross-domain fashion image retrieval

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gajic, Bojana Baldrich, Ramon Univ Autonoma Barcelona Comp Vis Ctr Edifici O UAB Bellaterra Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.

关键词： Task analysis Streaming media computer vision Image retrieval Biological system modeling conferences Training

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DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images 31

DeepGlobe 2018: A Challenge to Parse the Earth through Satel...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Demir, Ilke Koperski, Krzysztof Lindenbaum, David Pang, Guan Huang, Jing Bast, Saikat Hughes, Forest Tuia, Devis Raskar, Ramesh Facebook Menlo Pk CA 94025 USA DigitalGlobe Westminster CO USA CosmiQ Works Burnaby BC Canada Wageningen Univ Wageningen Netherlands MIT Media Lab Cambridge MA 02139 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with cvpr 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

关键词： Satellites Roads Buildings Task analysis Urban areas computer vision Image segmentation

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Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution 31

New Techniques for Preserving Global Structure and Denoising...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bei, Yijie Damian, Alex Hu, Shijia Menon, Sachit Ravi, Nikhil Rudin, Cynthia Duke Univ Durham NC 27706 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling. We summarize the techniques we developed for our second place entry in Track 1 (Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse Conditions), and seventh place entry in Track 3 (Realistic difficult) in the 2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural network architectures that specifically address the two challenges listed above: denoising and preservation of large-scale structure.

关键词： Image resolution Noise reduction Training Task analysis Neural networks computer architecture Knowledge engineering

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NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation 31

NU-Net: Deep Residual Wide Field of View Convolutional Neura...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Samy, Mohamed Amer, Karim Eissa, Kareem Shaker, Mahmoud ElHelw, Mohamed Nile Univ Ctr Informat Sci Giza Egypt

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder's feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full resolution information. NU-Net is a deep FCNN that is able to capture wide field of view global information around each pixel while maintaining localized full resolution information throughout the model. We evaluate our model on the Land Cover Classification and Road Extraction tracks in the DeepGlobe competition.

关键词： Image segmentation Semantics Roads computer architecture Convolution computer vision pattern recognition

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cornia, Marcella Baraldi, Lorenzo Serra, Giuseppe Cmcidara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Udine Udine Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.

关键词： Predictive models Visualization Measurement Feature extraction conferences computer architecture Computational modeling

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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Encapsulating the impact of transfer learning, domain knowledge and training strategies in deep-learning based architecture: A biometric based case study 31

Encapsulating the impact of transfer learning, domain knowle...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Singh, Avantika Nigam, Aditya Indian Inst Technol Mandi Mandi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K ( our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IIT-K. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.

关键词： Lenses Iris recognition Training Biological system modeling Knowledge engineering pattern recognition Analytical models

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