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Vehicle Re-Identification Based on Complementary Features

Vehicle Re-Identification Based on Complementary Features

引用

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

作者： Cunyuan Gao Yi Hu Yi Zhang Rui Yao Yong Zhou Jiaqi Zhao School of Computer Science and Technology China University of Mining and Technology China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this work, we present our solution to the vehicle reidentification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle ReIdentification demonstrated the advantage of our methods, we got 5-th place in the vehicle Re-ID track of AIC2020.

关键词： Feature extraction Training Task analysis Encoding Testing Information filters

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Rendering Natural Camera Bokeh Effect with Deep Learning

Rendering Natural Camera Bokeh Effect with Deep Learning

引用

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

作者： Andrey Ignatov Jagruti Patel Radu Timofte ETH Zurich Switzerland

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas. While DSLR and system camera lenses can render this effect naturally, mobile cameras are unable to produce shallow depth-of-field photos due to a very small aperture diameter of their optics. Unlike the current solutions simulating bokeh by applying Gaussian blur to image background, in this paper we propose to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras. For this, we present a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses. We use these images to train a deep learning model to reproduce a natural bokeh effect based on a single narrow-aperture image. The experimental results show that the proposed approach is able to render a plausible non-uniform bokeh even in case of complex input data with multiple objects. The dataset, pre-trained models and codes used in this paper are available on the project website: https://***/ ihnatova/***.

关键词： Cameras Rendering (computer graphics) Lenses Apertures Training Machine learning Visualization

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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...

引用

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...

引用

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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