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MultiNet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning 32

MultiNet++: Multi-stream feature aggregation and geometric l...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Chennupati, Sumanth Sistu, Ganesh Yogamani, Senthil Rawashdeh, Samir A Valeo North America United States Valeo Vision Systems University of Michigan-Dearborn United States

ISBN: (纸本)9781728125060

Multi-task learning is commonly used in autonomous driving for solving various visual perception tasks. It offers significant benefits in terms of both performance and computational complexity. Current work on multi-task learning networks focus on processing a single input image and there is no known implementation of multi-task learning handling a sequence of images. In this work, we propose a multi-stream multi-task network to take advantage of using feature representations from preceding frames in a video sequence for joint learning of segmentation, depth, and motion. The weights of the current and previous encoder are shared so that features computed in the previous frame can be leveraged without additional computation. In addition, we propose to use the geometric mean of task losses as a better alternative to the weighted average of task losses. The proposed loss function facilitates better handling of the difference in convergence rates of different tasks. Experimental results on KITTI, Cityscapes and SYNTHIA datasets demonstrate that the proposed strategies outperform various existing multi-task learning solutions. © 2019 ieee.

关键词： Learning systems

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Fine-Grained recognition in High-throughput Phenotyping

Fine-Grained Recognition in High-throughput Phenotyping

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Beichen Lyu Stuart D. Smith Keith A. Cherkauer Purdue University West Lafayette IN

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Fine-Grained recognition aims to classify sub-category objects such as bird species and car models from imagery. In High-throughput Phenotyping, the required task is to classify individual plant cultivars to assist plant breeding, which has posed three challenges: 1) it is easy to overfit complex features and models, 2) visual conditions change during and between image collection opportunities, and 3) analysis of thousands of cultivars require high-throughput data collection and analysis. To tackle these challenges, we propose a simple but intuitive descriptor, Radial Object Descriptor, to represent plant cultivar objects based on contour. This descriptor is invariant under scaling, rotation, and translation, as well as robust under changes to the plant's growth stage and camera's view angle. Furthermore, we complement this mid-level feature by fusing it with the low-level features (Histogram of Oriented Gradients) and deep features (ResNet-18), respectively. We extensively test our fusion approaches using two real world experiments. One experiment is on a novel benchmark dataset (HTP-Soy) in which we collect ~ 2, 000 high-resolution aerial images of outdoor soybean plots. Another experiment is on three datasets of indoor rosette plants. For both experiments, our fusion approaches achieve superior accuracies while maintaining better generalization as compared with traditional approaches.

关键词： Feature extraction Histograms Image recognition Task analysis pattern recognition Biological system modeling Visualization

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SOFEA: A Non-iterative and Robust Optical Flow Estimation Algorithm for Dynamic vision Sensors

SOFEA: A Non-iterative and Robust Optical Flow Estimation Al...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Weng Fei Low Zhi Gao Cheng Xiang Bharath Ramesh The N.1 Institute for Health National University of Singapore

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We introduce the single-shot optical flow estimation algorithm (SOFEA) to non-iteratively compute the continuous-time flow information of events produced from bio-inspired cameras such as the dynamic vision sensor (DVS). The output of a DVS is a stream of asynchronous spikes ("events"), transmitted at very minimal latency (1-10 μs), caused by local brightness changes. Due to this unconventional output, a continuous representation of events over time is invaluable to most applications using the DVS. To this end, SOFEA consolidates the spatio-temporal information on the surface of active events for flow estimation in a single-shot manner, as opposed to iterative methods in the literature. In contrast to previous works, this is also the first principled method towards finding locally optimal set of neighboring events for plane fitting using an adaptation of Prim's algorithm. Consequently, SOFEA produces flow estimates that are more accurate across a wide variety of scenes compared to state-of-the-art methods. A direct application of such flow estimation is rendering sharp event images using the set of active events at a given time, which is further demonstrated and compared to existing works (source code will be made available at our homepage after the review process).

关键词： Estimation Voltage control Streaming media Cameras Optical sensors Optical flow

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A Video Compression Framework Using an Overfitted Restoration Neural Network

A Video Compression Framework Using an Overfitted Restoratio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Gang He Chang Wu Lei Li Jinjia Zhou Xianglin Wang Yunfei Zheng Bing Yu Weiying Xie Xi’dian University Xi’an China Graduate School of Science and Engineering Hosei Univeristy Tokyo Japan Beijing Kuaishou Technology

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Many existing deep learning based video compression approaches apply deep neural networks (DNNs) to enhance the decoded video by learning the mapping between decoded video and raw video (ground truth). The big challenge is to train one well-fitted model (one mapping) for various video sequences. Different with the other applications such as image enhancement whose ground truth can only be obtained in the training process, the video encoder can always get the ground truth which is the raw video. It means we can train the model together with video compression and use one model for each sequence or even for each frame. The main idea of our approach is building a video compression framework (VCOR) using overfitted restoration neural network (ORNN). A lightweight ORNN is trained for a group of consecutive frames, so that it is overfitted to this group and achieves a strong restoration ability. After that, parameters of ORNN are transmitted to the decoder as a part of the encoded bitstream. At the decoder side, ORNN can perform the same strong restoration operation to the reconstructed frames. We participate in the CLIC2020 challenge on P-frame track as the team "West-World".

关键词： Video compression Image restoration Neural networks Training Decoding computer vision pattern recognition

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Fake News Detection using Higher-order User to User Mutual-attention Progression in Propagation Paths

Fake News Detection using Higher-order User to User Mutual-a...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rahul Mishra University of Stavanger Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Social media has become a very prominent source of news consumption. It brings forth multifaceted, multimodal and real-time information on a silver platter for the users. Fake news or rumor mongering on social media is one of the most challenging issues pertaining to present web. Previously, researchers have tried to classify news propagation paths on social media (e.g. Twitter) to detect fake news. However, they do not utilize latent relationships among users efficiently to model the influence of the users with high prestige on the other users, which is a very significant factor in information propagation. In this paper, we propose a novel Higher-order User to User Mutual-attention Progression (HiMaP) method to capture the cues related to authority or influence of the users by modelling direct and indirect (multi-hop) influence relationships among each pair of users, present in the propagation sequence. The proposed higher order attention trick is a novel contribution which can also be very effective in case of transformer architectures[30]. Our model not only outperforms the state-of-the-art methods on two publicly available Twitter datasets but also explains the propagation patterns pertaining to fake news by visualizing higher order mutual-attentions.

关键词： Feature extraction Twitter Linear programming computer vision Linguistics Time series analysis

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3D human pose estimation from multi person stereo 360◦ scenes 32

3D human pose estimation from multi person stereo 360◦ scen...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Shere, M. Kim, H. Hilton, A. Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

ISBN: (纸本)9781728125060

This paper presents a human tracking and 3D pose estimation algorithm for use with a pair of 360◦ cameras. We identify and track an individual throughout complex, multi-person scenes in both indoor and outdoor environments using appearance models and positional data, and produce a temporally consistent 3D skeleton by optimising a skeleton of realistic joint lengths over joint positions produce by Convolutional Pose Machines (CPMs). Our results show an average improvement of 22.67% over state of the art deep learning approaches for tracking, as well as reasonable estimates for pose using just two cameras. © 2019 ieee computer Society. All rights reserved.

关键词： Cameras

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Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints

Constraint-Aware Importance Estimation for Global Filter Pru...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yu-Cheng Wu Chih-Ting Liu Bo-Ying Chen Shao-Yi Chien NTU IoX Center National Taiwan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate the importance of each filter based on its impact to the loss and iteratively remove those with smaller values until the pruned network meets some resource constraints, such as the commonly used number (or ratio) of filter left. However, when there is a more practical constraint like the total number of FLOPs, they ignore its relation to the estimation of filter importance. We propose a novel method called Constraint-Aware Importance Estimation (CAIE) that integrates information of the impact on the given resource into the original importance estimation only based on loss when pruning each filter. Moreover, our CAIE can be generalized to the pruning problem under multiple resource constraints simultaneously. Extensive experiments show that under the same multiple resource constraints, the model pruned with our CAIE method can not only accurately meet the constraints but also achieve the optimal performance results when comparing to existing state-of-the-art methods.

关键词： Estimation Computational modeling Training Optimization Performance evaluation Taylor series computer vision

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Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild

Multiple Transfer Learning and Multi-label Balanced Training...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sijie Ji Kai Wang Xiaojiang Peng Jianfei Yang Zhaoyang Zeng Yu Qiao Nanyang Technological University Singapore ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science Sun Yat-Sen University China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper 1 presents SIAT-NTU solution and results of facial action unit (AU) detection in the EmotiNet Challenge 2020. The task aims to detect 23 AUs from facial images in the wild, and its main difficulties lie in the imbalanced AU distribution and discriminative feature learning. We tackle these difficulties from the following aspects. First, to address the unconstrained heterogeneity of in-the-wild images, we detect and align faces with multi-task convolutional neural networks (MTCNN). Second, by using multiple transfer strategies, we pre-train large CNNs on multiple related datasets, e.g. face recognition datasets and facial expression datasets, and fine-tune them on the EmotiNetdataset. Third, we employ a multi-label balanced sampling strategy and a weighted loss to mitigate the imbalance problem. Last but not the least, to further improve performance, we ensemble multiple models and optimize the thresholds for each AU. Our proposed solution achieves an accuracy of 90.13% and F1 of 44.10% in the final test phase. Our Code is available at: https://***/kaiwang960112/enc2020_au_detection.

关键词： Gold Task analysis Feature extraction Face recognition Training Mathematical model Muscles

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Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Krati Gupta Anshul Thakur Michael Goldbaum Siamak Yousefi School of Computing & Electrical Engineering Indian Institute of Technology Mandi Mandi India Department of Ophthalmology University of California San Diego USA Department of Ophthalmology University of Tennessee Health Science Center Memphis USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： Visualization Diseases Machine learning Dictionaries Data models Neural networks pattern recognition

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Learning feature representations for look-alike images 32

Learning feature representations for look-alike images

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32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Takmaz, Ayça Probst, Thomas Paudel, Danda Pani van Gool, Luc Computer Vision Lab ETH Zürich Switzerland

ISBN: (纸本)9781728125060

Human perception of visual similarity relies on information varying from low-level features such as texture and color, to high-level features such as objects and elements. While generic features learned for image or face recognition tasks somewhat correlate with the perceived visual similarity, they are found to be inadequate f or matching lookalike images. In this paper, we learn the 'look-alike feature' embedding, capable of representing the perceived image similarity, by fusing low- and high-level features within a modified CNN encoder architecture. This encoder is trained using the triplet loss paradigm on look-alike image pairs. Our findings demonstrate that combining features f rom different layers across the network is beneficial for look-alike image matching, and clearly outperforms the standard pretrained networks followed by fine-tuning. Furthermore, we show that the learned similarities are meaningful, and capture color, shape, facial or holistic appearance patterns, depending upon context and image modalities. © 2019 ieee computer Society. All rights reserved.

关键词： Network layers

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