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A compact 3D camera suited for mobile and embedded vision applications

A compact 3D camera suited for mobile and embedded vision ap...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Mattoccia, Stefano Marchio, Ilario Casadio, Marco Univ Bologna Sch Engn & Architecture Dept Comp Sci & Engn Bologna Italy

ISBN: (纸本)9781479943098

Recent years have seen the widespread diffusion of 3D sensors, mainly based on active technologies such as structured light and Time-of-Flight, enabling the development of very interesting 3D vision applications. This paper describes a compact 3D camera based on passive stereo vision technology suited for mobile/embedded vision applications. Our 3D camera is very compact, the overall area of the processing unit is smaller than a business card, lightweight, it weights less than 100 g including lenses, has a reduced power consumption, about 2 Watt processing stereo pairs at 30+ fps, and can be easily configured with different baselines and processing units according to specific application requirements. The overall design is mapped on a low cost FPGA, making the hardware design easily portable to other reconfigurable devices, and allows us to obtain in real-time accurate and dense depth maps according to state-of-the-art stereo vision algorithms.

关键词： 3D stereo vision FPGA RGBD depth sensor

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TAEN: Temporal Aware Embedding Network for Few-Shot Action recognition

TAEN: Temporal Aware Embedding Network for Few-Shot Action R...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ben-Ari, Rami Nacson, Mor Shpigel Azulai, Ophir Barzelay, Udi Rotman, Daniel OriginAI Haifa Israel Technion Haifa Israel IBM Res AI Haifa Israel

ISBN: (纸本)9781665448994

Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.

关键词： Training computer vision conferences Semantics Benchmark testing Extraterrestrial measurements pattern recognition

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Behavior and Personality Analysis in a nonsocial context Dataset 31

Behavior and Personality Analysis in a nonsocial context Dat...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dotti, Dario Popa, Mirela Asteriadis, Stylianos Maastricht Univ Dept Data Sci & Knowledge Engn Maastricht Netherlands

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Personality recognition using nonverbal behavioral cues is a challenging task in the Affective Computing field. The majority of existing methods investigate personality assessment in social contexts, such as crowded places or social events, but ignore the role of behaviors as well as personality in nonsocial situations (i.e. during individual activities). In this paper we introduce a novel dataset for behavior understanding and personality recognition in a nonsocial context. Forty-six participants were recorded in an unconstrained indoor space, related to a smart home environment, performing six tasks resembling Activities of Daily Living (ADL). During the experiment, personality scores were collected using self-assessment questionnaires. Furthermore, a temporal framework using a Long-Short Term Memory (LSTM) network is proposed to map nonverbal behavioral features to participants' personality labels. Our experiments showed that nonverbal behaviors are important predictors of personality, confirming theories from the personality psychology field.

关键词： Skeleton Task analysis Psychology Feature extraction Problem-solving Computational modeling computer vision

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Attenuating Catastrophic Forgetting by Joint Contrastive and Incremental Learning

Attenuating Catastrophic Forgetting by Joint Contrastive and...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ferdinand, Quentin Clement, Benoit Oliveau, Quentin Le Chenadec, Gilles Papadakis, Panagiotis Naval Grp Res Cherbourg En Cotentin France ENSTA Bretagne Lab STICC UMR 6285 Brest France IMT Atlantique Lab STICC UMR 6285 Brest France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In class incremental learning, discriminative models are trained to classify images while adapting to new instances and classes incrementally. Training a model to adapt to new classes without total access to previous class data, however, leads to the known problem of catastrophic forgetting of the previously learnt classes. To alleviate this problem, we show how we can build upon recent progress on contrastive learning methods. In particular, we develop an incremental learning approach for deep neural networks operating both at classification and representation level which alleviates forgetting and learns more general features for data classification. Experiments performed on several datasets demonstrate the superiority of the proposed method with respect to well known state-of-the-art methods.

关键词： Learning systems Training Deep learning Adaptation models computer vision conferences Computational modeling

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GAN-based vision Transformer for High-Quality Thermal Image Enhancement

GAN-based Vision Transformer for High-Quality Thermal Image ...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Marnissi, Mohamed Amine Fathallah, Abir Univ Sfax Ecole Natl Ingn Sfax Sfax 3038 Tunisia Inst Polytech Paris Samovar CNRS Telecom SudParis 9 Rue Charles Fourier F-91011 Evry France

ISBN: (纸本)9798350302493

Generative Adversarial Networks (GANs) have shown an outstanding ability to generate high-quality images with visual realism and similarity to real images. This paper presents a new architecture for thermal image enhancement. Precisely, the strengths of architecture-based vision transformers and generative adversarial networks are exploited. The thermal loss feature introduced in our approach is specifically used to produce high-quality images. Thermal image enhancement also relies on fine-tuning based on visible images, resulting in an overall improvement in image quality. A visual quality metric was used to evaluate the performance of the proposed architecture. Significant improvements were found over the original thermal images and other enhancement methods established on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin regarding different versions of the YOLO detector.

关键词： Image enhancement

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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

Pano3D: A Holistic Benchmark and a Solid Baseline for 360° ...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Albanis, Georgios Zioulis, Nikolaos Drakoulis, Petros Gkitsas, Vasileios Sterzentsenko, Vladimiros Alvarez, Federico Zarpalas, Dimitrios Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781665448994

Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degrees depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

关键词： computer vision conferences Estimation Benchmark testing Solids pattern recognition

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

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RIT-18: A Novel Dataset for Compositional Group Activity Understanding

RIT-18: A Novel Dataset for Compositional Group Activity Und...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Junwen Hao, Haiting Hong, Hanbin Kong, Yu Rochester Inst Technol Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781728193601

Group activity understanding is a challenging task as multiple people are involved, and their relations may vary over time. Currently, the literature of group activity is limited to group activity recognition, because videos are trimmed in very short duration and focus on a single activity. This slows down the progress in the group activity domain. In this paper, we propose a new large-scale untrimmed compositional group activity dataset RIT-18 based on the volleyball games captured from YouTube. Each clip in our dataset depicts an entire rally which spans the duration from serve to a point being scored. Comprehensive annotations including group activity labels, temporal boundaries of activities, key persons, and winning teams are provided. We describe group activity recognition, future activity anticipation, and rally-level winner prediction challenges, and evaluate several baseline methods over these challenges. We report their performance on our dataset and demonstrate further efforts need to be made. The dataset is available at https://***/actionlab/rit-18.

关键词： computer vision

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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

A Vision-based System for Traffic Anomaly Detection using De...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA

ISBN: (纸本)9781665448994

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.

关键词： Deep learning Adaptation models Roads conferences Estimation Real-time systems pattern recognition

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Boosting Unconstrained Face recognition with Auxiliary Unlabeled Data

Boosting Unconstrained Face Recognition with Auxiliary Unlab...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shi, Yichun Jain, Anil K. Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665448994

In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets. However, since the faces in these datasets usually contain limited degree and types of variation, the resulting trained models generalize poorly to more realistic unconstrained face datasets. While collecting labeled faces with larger variations could be helpful, it is practically infeasible due to privacy and labor cost. In comparison, it is easier to acquire a large number of unlabeled faces from different domains, which could be used to regularize the learning of face representations. We present an approach to use such unlabeled faces to learn generalizable face representations, where we assume neither the access to identity labels nor domain labels for unlabeled images. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can (i) lead to an appreciable gain in recognition performance and (ii) outperform the supervised baseline when combined with less than half of the labeled data. Compared with the state-of-the-art face recognition methods, our method further improves their performance on challenging benchmarks, such as IJBB, IJB-C and IJB-S.

关键词： Training Visualization Privacy computer vision Protocols Face recognition conferences

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