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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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SBNet: Segmentation-based Network for Natural Language-based Vehicle Search

SBNet: Segmentation-based Network for Natural Language-based...

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

作者： Lee, Sangrok Woo, Taekang Lee, Sang Hun MODULABS Seoul South Korea NAVER Corp Seongnam Si South Korea Kookmin Univ Seoul South Korea

ISBN: (纸本)9781665448994

Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied to various areas including police searching for a suspect vehicle. However, it is challenging due to the ambiguity of language descriptions and the difficulty of processing multi-modal data. To tackle this problem, we propose a deep neural network called SBNet that performs natural language-based segmentation for vehicle retrieval. We also propose two task-specific modules to improve performance: a substitution module that helps features from different domains to be embedded in the same space and a future prediction module that learns temporal information. SBnet has been trained using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three unique natural language descriptions each and tested 530 unique vehicle tracks and their corresponding query sets. SBNet achieved a significant improvement over the baseline in the natural language-based vehicle tracking track in the AI City Challenge 2021. Source Code: https://***/lsrock1/nlp_search

关键词： Deep learning Image segmentation computer vision Law enforcement conferences Urban areas Natural languages

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Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems

Towards Accurate Visual and Natural Language-Based Vehicle R...

引用

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

作者： Khorramshahi, Pirazh Rambhatla, Sai Saketh Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8th in the public leaderboard for both tracks.

关键词： Training Visualization computer vision Adaptation models conferences Urban areas Cameras

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Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment

Self-Supervised Multi-Task Pretraining Improves Image Aesthe...

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

作者： Pfister, Jan Kobs, Konstantin Hotho, Andreas Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665448994

Neural networks for Image Aesthetic Assessment are usually initialized with weights of pretrained ImageNet models and then trained using a labeled image aesthetics dataset. We argue that the ImageNet classification task is not well-suited for pretraining, since content based classification is designed to make the model invariant to features that strongly influence the image's aesthetics, e.g. style-based features such as brightness or contrast. We propose to use self-supervised aesthetic-aware pretext tasks that let the network learn aesthetically relevant features, based on the observation that distorting aesthetic images with image filters usually reduces their appeal. To ensure that images are not accidentally improved when filters are applied, we introduce a large dataset comprised of highly aesthetic images as the starting point for the distortions. The network is then trained to rank less distorted images higher than their more distorted counterparts. To exploit effects of multiple different objectives, we also embed this task into a multi-task setting by adding either a self-supervised classification or regression task. In our experiments, we show that our pretraining improves performance over the ImageNet initialization and reduces the number of epochs until convergence by up to 47 %. Additionally, we can match the performance of an ImageNet-initialized model while reducing the labeled training data by 20 %. We make our code, data, and pretrained models available.

关键词： computer vision conferences Computational modeling Neural networks Training data Distortion Data models

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Vehicle Re-Identification based on Ensembling Deep Learning Features including a Synthetic Training Dataset, Orientation and Background Features, and Camera Verification.

Vehicle Re-Identification based on Ensembling Deep Learning ...

引用

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

作者： Fernandez, Marta Moral, Paula Garcia-Martin, Alvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid Spain

ISBN: (纸本)9781665448994

Vehicle re-identification has the objective of finding a specific vehicle among different vehicle crops captured by multiple cameras placed at multiple intersections. Among the different difficulties, high intra-class variability and high inter-class similarity can be highlighted. Moreover, the resolution of the images can be different, which also means a challenge in the re-identification task. Intending to face these problems, we use as baseline our previous work based on obtaining different deep learning features and ensembling them to get a single, stable and robust feature vector. It also includes post-processing techniques that explode all the information provided by the CityFlowV2-ReID dataset, including a re-ranking step. Then, in this paper, several newly included improvements are described. Background and orientation similarity matrices are added to the system to reduce bias towards these characteristics. Furthermore, we take into account the camera labels to penalize the gallery images that share camera with the query image. Additionally, to improve the training step, a synthetic dataset is added to the original one.

关键词： Training Deep learning Image resolution Image color analysis Cameras Feature extraction pattern recognition

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Phase Selective Convolution

Phase Selective Convolution

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

作者： Lin, Jamie Menjay Noorzad, Parham Yang, Yang Kwak, Nojun Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665448994

This paper introduces Phase Selective Convolution (PSC), an enhanced convolution for more deliberate utilization of activations in convolutional networks. Unlike conventional use of convolutions with activation functions, PSC preserves the full space of activations while supporting desirable model nonlinearity. Similar to several other network operations, e.g., the ReLU operation, at the time of their introduction, PSC may not execute as efficiently on platforms without hardware specialization support. As a first step in addressing the need for optimization, we propose a hardware acceleration scheme to enable the intended efficiency for PSC execution. Moreover, we propose a PSC deployment strategy, with which PSC is applied only to selected layers of the networks, to avoid excessive increase in the total model size. To evaluate the results, we apply PSC as a drop-in replacement for selected convolution layers in several networks without affecting their macro network architectures. In particular, PSC-enhanced ResNets achieve higher accuracies by 1.0-2.0% and 0.7-1.0% on CIFAR-100 and ImageNet, respectively, in Pareto efficiency. PSC-enhanced MobileNets (V2 and V3 Large) and MobileNetV3 (Small) achieve 0.9-1.0% and 1.8% accuracy gains, respectively, on ImageNet at little (0.2-0.7%) total model size increase.

关键词： Measurement Tensors Convolution Estimation Network architecture Search problems pattern recognition

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Noise Conditional Flow Model for Learning the Super-Resolution Space

Noise Conditional Flow Model for Learning the Super-Resoluti...

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

作者： Kim, Younggeun Son, Donghee Seoul Natl Univ Seoul South Korea Lomin Inc Seoul South Korea Deepest Seoul South Korea

ISBN: (纸本)9781665448994

Fundamentally, super-resolution is ill-posed problem because a low-resolution image can be obtained from many high-resolution images. Recent studies for super-resolution cannot create diverse super-resolution images. Although SRFlow tried to account for ill-posed nature of the super-resolution by predicting multiple high-resolution images given a low-resolution image, there is room to improve the diversity and visual quality. In this paper, we propose Noise Conditional flow model for Super-Resolution, NCSR, which increases the visual quality and diversity of images through noise conditional layer. To learn more diverse data distribution, we add noise to training data. However, low-qualit . , images are resulted from adding noise. We propose the noise conditional layer to overcome this phenomenon. The noise conditional layer makes our model generate more diverse images with higher visual quality than other works. Furthermore, we show that this layer can overcome data distribution mismatch, a problem that arises in normalizing flow models. With these benefits, NCSR outperforms baseline in diversity and visual quality and achieves better visual quality than traditional GAN-based models. We also get outperformed scores at NTIRE 2021 challenge [21].

关键词： Training Degradation Visualization computer vision Computational modeling conferences Superresolution

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Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos

Towards Automated and Marker-less Parkinson Disease Assessme...

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

作者： Mehta, Deval Asif, Umar Hao, Tian Bilal, Erhan von Cavallar, Stefan Harrer, Stefan Rogers, Jeffrey IBM Res Melbourne Vic Australia IBM Res Yorktown Hts NY 10598 USA Digital Hlth Cooperat Res Ctr Camperdown NSW Australia

ISBN: (纸本)9781665448994

This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinson's Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinson's disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine.

关键词： Legged locomotion Deep learning Training Telemedicine Sociology pattern recognition Task analysis

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City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad...

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

作者： Liu, Chong Zhang, Yuqi Luo, Hao Tang, Jiasheng Chen, Weihua Xu, Xianzhe Wang, Fan Li, Hao Shen, Yi-Dong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDE-Tracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard (1). The code will be released later.

关键词： Matched filters computer vision Target tracking conferences Urban areas Feature extraction Cameras

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EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task.

EarthNet2021: A large-scale dataset and challenge for Earth ...

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

作者： Requena-Mesa, Christian Benson, Vitus Reichstein, Markus Runge, Jakob Denzler, Joachim Max Planck Inst Biogeochem Biogeochem Integrat Jena Germany German Aerosp Ctr DLR Inst Data Sci Jena Germany Univ Jena Comp Vis Grp Jena Germany Michael Stifel Ctr Jena Data Driven & Simulat Sci Jena Germany Tech Univ Berlin Berlin Germany

ISBN: (纸本)9781665448994

Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery at 20 m resolution, matching topography and mesoscale (1.28 km) meteorological variables packaged into 32000 samples. Additionally we frame EarthNet2021 as a challenge allowing for model intercomparison. Resulting forecasts will greatly improve (> x50) over the spatial resolution found in numerical models. This allows localized impacts from extreme weather to be predicted, thus supporting downstream applications such as crop yield prediction, forest health assessments or biodiversity monitoring. Find data, code, and how to participate at ***.

关键词： Earth Training Satellites Weather forecasting Surface topography Numerical models pattern recognition

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Spacecraft Time-Series Anomaly Detection Using Transfer Learning

Spacecraft Time-Series Anomaly Detection Using Transfer Lear...

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

作者： Baireddy, Sriram Desai, Sundip R. Mathieson, James L. Foster, Richard H. Chan, Moses W. Comer, Mary L. Delp, Edward J. Purdue Univ Video & Image Proc Lab VIPER W Lafayette IN 47907 USA Adv Technol Ctr Lockheed Martin Space Sunnyvale CA USA

ISBN: (纸本)9781665448994

Anomaly detection in telemetry channels is a high priority for spacecraft, especially when considering the harsh environment of space and the magnitude of launch and operation costs. Traditional spacecraft anomaly detection methods are limited in scope and rely on domain experts to correctly determine abnormal behavior. However, with thousands of distinct telemetry channels being transmitted, the amount of data is difficult to monitor manually. Deep learning models can be used to learn the normal behavior of the telemetry channels and flag or label any deviations. The problem is that we have to train a unique model for each channel to ensure best performance. With the large number of channels to monitor, this may not always be possible. We propose using principles of transfer learning to quickly adapt a general pre-trained model to any specific telemetry channel, greatly reducing the number of unique models needed and reducing the training time for each model. We present the results of our approach on the NASA SMAP/MSL dataset to show that we can achieve performance comparable to state-of-the-art anomaly detection methods.

关键词： Space vehicles Training Adaptation models Transfer learning Predictive models Data models pattern recognition

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