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The 1st Agriculture-vision Challenge: Methods and Results

The 1st Agriculture-Vision Challenge: Methods and Results

引用

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

作者： Chiu, Mang Tik Xu, Xingqian Wang, Kai Hobbs, Jennifer Hovakimyan, Naira Huang, Thomas S. Shi, Honghui Wei, Yunchao Huang, Zilong Schwing, Alexander Brunner, Robert Dozier, Ivan Dozier, Wyatt Ghandilyan, Karen Wilson, David Park, Hyunseong Kim, Junhee Kim, Sungho Liu, Qinghui Kampffmeyer, Michael C. Jenssen, Robert Salberg, Arnt B. Barbosa, Alexandre Trevisan, Rodrigo Zhao, Bingchen Yu, Shaozuo Yang, Siwei Wang, Yin Sheng, Hao Chen, Xiao Su, Jingyi Rajagopal, Ram Ng, Andrew Van Thong Huynh Kim, Soo-Hyung Na, In-Seop Baid, Ujjwal Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Tang, Jianyu UIUC Urbana IL 61801 USA Intelinair Indianapolis IN USA Univ Oregon Eugene OR 97403 USA Agcy Def Dev Seoul South Korea DGIST Daegu South Korea Norwegian Comp Ctr Oslo Norway UiT Arctic Univ Norway Oslo Norway Tongji Univ Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chegg Inc Santa Clara CA USA Chonnam Natl Univ Gwangju South Korea Chosun Univ Gwangju South Korea SGGS Inst Engn & Technol Nanded India Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

The first Agriculture-vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task;the link can be found here.

关键词： Semantics Image segmentation pattern recognition conferences Agriculture Computational modeling computer vision

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Semi-Supervised Learning with Scarce Annotations

Semi-Supervised Learning with Scarce Annotations

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

作者： Rebuffi, Sylvestre-Alvise Ehrhardt, Sebastien Han, Kai Vedaldi, Andrea Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9781728193601

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

关键词： 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/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ji, Sijie Wang, Kai Peng, Xiaojiang Yang, Jianfei Zeng, Zhaoyang Qiao, Yu Nanyang Technol Univ Singapore Singapore Singapore Chinese Acad Sci Shenzhcn Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781728193601

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： Convolutional neural networks

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CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

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

作者： Wang, Zhengwei She, Qi Chalasani, Tejo Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781728193601

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： computer vision

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Plastic Surgery: An Obstacle for Deep Face recognition?

Plastic Surgery: An Obstacle for Deep Face Recognition?

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

作者： Rathgeb, Christian Dogan, Didem Stockhardt, Fabian De Marsico, Maria Busch, Christoph Hsch Darmstadt Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany Sapienza Univ Roma Dipartimento Informat Rome Italy

ISBN: (纸本)9781728193601

The impacts of plastic surgery on face recognition systems have been investigated in the past decade by many researchers. Diverse well-known face recognition approaches, e.g. based on PCA or LBP, have been benchmarked mostly on the web-collected IIITD plastic surgery face database. Generally, significant performance drops were reported when comparing facial images taken before and after plastic surgeries. On the one side, some researchers reported problems with said plastic surgery database, i.e. the presence of low quality images. On the other side, the applied methods no longer reflect the state-of-the-art in face recognition. This calls for evaluating the impact of plastic surgery on state-of-the-art deep face recognition systems anew considering high quality imagery of most relevant plastic surgeries. This work introduces the new Hochschule Darmstadt (HDA) plastic surgery database of facial images taken before and after surgery. This database vastly complies with the quality requirements defined by the International Civil Aviation Organization (ICAO) for electronic travel documents and comprises face images of the five most frequently applied facial plastic surgeries. The HDA plastic surgery database, the IIITD plastic surgery database, and a non-surgery database, i.e. ICAO-compliant subsets of the FRGCv2 and FERET datasets, are used for comparative verification and identification evaluations which are conducted using the commercial Cognitec FaceVACS system and the open-source ArcFace system. The obtained results suggest that the impact of plastic surgery on deep face recognition systems is less significant than that observed for previously benchmarked methods.

关键词： Database systems

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Challenges in Recognizing Spontaneous and Intentionally Expressed Reactions to Positive and Negative Images

Challenges in Recognizing Spontaneous and Intentionally Expr...

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

作者： Healey, Jennifer Wang, Haoliang Chhaya, Niyati Adobe Res San Jose CA 95110 USA Adobe Res Bangalore Karnataka India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

This paper presents a preliminary exploration of the challenges of automatically recognizing positive and negative facial expressions in both spontaneous and intentionally expressed conditions. Instead of recognizing iconic basic emotion states, which we have found to be less common in typical human computer interaction, we instead attempted to recognize only positive versus negative states. Our hypothesis was that this would prove more accurate if participants intentionally expressed their feelings. Our study consisted of analyzing video from seven participants, each participating in two sessions. Participants were asked to view 20 images, 10 positive and 10 negative, selected from the OASIS image data set. In the first session participants were instructed to react normally, while in the second session they were asked to intentionally express the emotion they felt when looking at each image. We extracted facial action coding units (AUs) from the recorded video and found that on average, intentionally expressed emotions generated 33% more AU intensity across action units associated with both negative emotions (AU1, AU2, AU4 and AU5) and 117% more intensity for AUs associated with positive emotions (AU6 and AU12). We also show that wide variation exists both in average participant responses across images and in individual reactions to images and that simply taking a ration of our identified action units is not sufficient to determine if a response is positive or negative even in the intentionally expressed case.

关键词： Emotion recognition Lakes Face recognition Encoding Image recognition Gold

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Semi-Supervised Hyperspectral Object Detection Challenge Results - PBVS 2022

Semi-Supervised Hyperspectral Object Detection Challenge Res...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Aneesh Rangnekar Zachary Mulhollan Anthony Vodacek Matthew Hoffman Angel Sappa Erik Blasch Jun Yu Liwen Zhang Shenshen Du Hao Chang Keda Lu Zhong Zhang Fang Gao Ye Yu Feng Shuang Lei Wang Qiang Ling Pranjay Shyam Kuk-Jin Yoon Kyung-Soo Kim Rochester Institute of Technology Rochester NY USA ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain US Air Force Research Lab Rome NY

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the computer vision and pattern recognition (cvpr) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results.

关键词： Training computer vision conferences Training data Object detection Semisupervised learning Transformers

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A Geometric ConvNet on 3D Shape Manifold for Gait recognition

A Geometric ConvNet on 3D Shape Manifold for Gait Recognitio...

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

作者： Hosni, Nadia Ben Amor, Boulbaba Univ Manouba CRISTAL Manouba Tunisia Univ Lille CNRS 9189 IMT Lille Douai CRIStAL Lille France Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728193601

In this work we propose a geometric deep convolutional auto-encoder (DCAE) for the purpose of gait recognition by analyzing time-varying 3D skeletal data. Sequences are viewed as time-parameterized trajectories on the Kendall shape space S, results of modding out shape-preserving transformations (scaling, translations and rotations). The accommodation of ConvNet architectures to properly approximate manifold-valued trajectories on the underlying non-linear space S is a must. Thus, we make use of geometric steps prior to the encoding-decoding scheme. That is, shape trajectories are first log-mapped to tangent spaces attached to the shape space at a time-varying average trajectory mu, then, obtained vectors are transported to a common tangent space T mu((0)) (S) at the starting point of mu. Without applying any prior temporal alignment (e.g. Dynamic Time Warping) or modeling (e.g. HMM, RNN), the transported trajectories are then fed to a convolutional auto-encoder to build subject-specific latent spaces. The proposed approach was tested on two publicly available datasets. Our approach outperforms existing approaches on CMU gait dataset, while performances on UPCV K2 are comparable to existing approaches. We demonstrate that combining geometric invariance (i.e. Kendall's representation) with our data-driven ConvNet model is suitable to alleviate spatial and temporal variability, respectively.

关键词： Trajectories

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AI City Challenge 2020-computer vision for Smart Transportation Applications

AI City Challenge 2020-Computer Vision for Smart Transportat...

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

作者： Chang, Ming-Ching Chiang, Chen-Kuo Tsai, Chun-Ming Chang, Yun-Kai Chiang, Hsuan-Lun Wang, Yu-An Chang, Shih-Ya Li, Yun-Lun Tsai, Ming-Shuin Tseng, Hung-Yu SUNY Albany Albany NY 12222 USA Natl Chung Cheng Univ Minxiong Taiwan Univ Taipei Taipei Taiwan

ISBN: (纸本)9781728193601

We present methods developed in our participation of the AI City 2020 Challenge (AIC20) and report evaluation results in this contest. With the blooming of AI computer vision techniques, vehicle detection, tracking, identification, and counting all have advanced significantly. However, whether these technologies are ready for real-world smart transportation usage is still a open question. The goal of this work is to apply and integrate state-of-the-art techniques for solving the challenge problems under a standardized setup and evaluation. We participated all 4 AIC20 challenge tracks (T1 to T4). In T1 challenge, we perform vehicle counting by associating deep features extracted from Mask-RCNN detections and tracklets, followed by vehicle movement zone matching. In T2 challenge, we perform vehicle type and color classification and then rank matching vehicles using a PGAM re-id network. In T3 challenge, we proposed a new Multi-Camera Tracking Network (MTCN) that takes single-camera vehicle tracking as input, and performs multi-camera tracklet fusion and linking, by jointly optimizing the matching of vehicle appearance and physical features. In T4 challenge, we adopt a leading method based on perspective detection and spatial-temporal matrix discriminating, and improve it with background modeling for traffic anomaly detection. We achieved top-6 and top-4 performance for T3 and T4 challenges respectively in the AIC20 general leaderboard.

关键词： computer vision

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PI-Net: A Deep Learning Approach to Extract Topological Persistence Images

PI-Net: A Deep Learning Approach to Extract Topological Pers...

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

作者： Som, Anirudh Choi, Hongjun Ramamurthy, Karthikeyan Natesan Buman, Matthew P. Turaga, Pavan Arizona State Univ Sch Arts Media & Engn Tempe AZ 85287 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA IBM Res Albany NY USA Arizona State Univ Coll Hlth Solut Tempe AZ 85287 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://***/anirudhsom/PI-Net.

关键词： Machine learning computer architecture computer vision Task analysis Topology Feature extraction Data models

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