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Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

Self-Supervised Learning of Remote Sensing Scene Representat...

引用

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

作者： Stojnic, Vladan Risojevic, Vladimir Univ Banja Luka Fac Elect Engn Banja Luka Bosnia & Herceg

ISBN: (纸本)9781665448994

In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks.

关键词： Visualization Image segmentation Image coding conferences Object detection Feature extraction pattern recognition

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrea Panzino Simone Maurizio La Cava Giulia Orrù Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： computer vision Face recognition conferences Employment Process control Detectors Market research

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Box-Level Tube Tracking and Refinement for Vehicles Anomaly Detection

Box-Level Tube Tracking and Refinement for Vehicles Anomaly ...

引用

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

作者： Wu, Jie Wang, Xionghui Xiao, Xuefeng Wang, Yitong ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9781665448994

Traffic Anomaly detection is an essential computer vision task and plays a critical role in video structure analysis and urban traffic analysis. In this paper, we propose a box-level tracking and refinement algorithm to identify anomaly detection in road scenes. We first link the detection results to construct candidate spatio-temporal tubes via greedy search. Then the box-level refinement scheme is introduced to employ auxiliary detection cues to promote the abnormal predictions, which consists of spatial fusion, still-thing filter, temporal fusion, and feedforward optimization. Still-thing filter and feedforward optimization employ complementary detection concepts to promote the abnormal predictions, which helps determine an accurate abnormal period. The experimental results show that our approach is superior in the Traffic Anomaly Detection Track test set of the NVIDIA AI CITY 2021 CHALLENGE, which ranked second in this competition, with a 93.18% F1-score and 3.1623 root mean square error. It reveals that the proposed approach contributes to fine-grained anomaly detection in actual traffic accident scenarios and promoting the development of intelligent transportation.

关键词： computer vision Urban areas Transportation Electron tubes Artificial intelligence Vehicle dynamics Task analysis

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Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Beyond VQA: Generating Multi-word Answers and Rationales to ...

引用

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

作者： Dua, Radhika Kancheti, Sai Srinivas Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9781665448994

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

关键词： Deep learning Visualization Vocabulary Computational modeling conferences computer architecture Knowledge discovery

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Table Tennis Stroke recognition Using Two-Dimensional Human Pose Estimation

Table Tennis Stroke Recognition Using Two-Dimensional Human ...

引用

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

作者： Kulkarni, Kaustubh Milind Shenoy, Sucheth

ISBN: (纸本)9781665448994

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player's strokes, which is extremely insightful for performance improvement.

关键词： Training Deep learning computer vision Computational modeling Pose estimation Neural networks Stroke (medical condition)

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Guidance Network with Staged Learning for Image enhancement

Guidance Network with Staged Learning for Image enhancement

引用

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

作者： Liang, Luming Zharkov, Ilya Amjadi, Faezeh Joze, Hamid Reza Vaezi Pradeep, Vivek Microsoft One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781665448994

Many important yet not fully resolved problems in computational photography and image enhancement, e.g. generating well-lit images from their low-light counterparts or producing RGB images from their RAW camera inputs share a common nature: discovering a color mapping between input pixels to output pixels based on both global information and local details. We propose a novel deep neural network architecture to learn the RAW to RGB mapping based on this common nature. This architecture consists of both global and local sub-networks, where the first sub-network focuses on determining illumination and color mapping, the second sub-network deals with recovering image details. The result of the global network serves as a guidance to the local network to form the final RGB images. Our method outperforms state-of-the-art with a significantly smaller size of network features on various image enhancement tasks.

关键词： Training Photography Image color analysis Lighting computer architecture Cameras Service-oriented architecture

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Distill on the Go: Online knowledge distillation in self-supervised learning

Distill on the Go: Online knowledge distillation in self-sup...

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

作者： Bhat, Prashant Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands

ISBN: (纸本)9781665448994

Self-supervised learning solves pretext prediction tasks that do not require annotations to learn feature representations. For vision tasks, pretext tasks such as predicting rotation, solving jigsaw are solely created from the input data. Yet, predicting this known information helps in learning representations useful for downstream tasks. However, recent works have shown that wider and deeper models benefit more from self-supervised learning than smaller models. To address the issue of self-supervised pre-training of smaller models, we propose Distill-on-the-Go (DoGo), a self-supervised learning paradigm using single-stage online knowledge distillation to improve the representation quality of the smaller models. We employ deep mutual learning strategy in which two models collaboratively learn from each other to improve one another. Specifically, each model is trained using self-supervised learning along with distillation that aligns each model's softmax probabilities of similarity scores with that of the peer model. We conduct extensive experiments on multiple benchmark datasets, learning objectives, and architectures to demonstrate the potential of our proposed method. Our results show significant performance gain in the presence of noisy and limited labels, and in generalization to out-of-distribution data.

关键词： computer vision Annotations conferences computer architecture Performance gain Benchmark testing pattern recognition

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Audio Provenance Analysis in Heterogeneous Media Sets

Audio Provenance Analysis in Heterogeneous Media Sets

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Milica Gerhardt Luca Cuccovillo Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a framework for Audio Provenance Analysis, addressing the complex challenge of ana-lyzing heterogeneous sets of audio items without requiring any prior knowledge of their content. Our framework applies a novel approach that combines partial audio matching and phylogeny techniques. It constructs directed acyclic graphs to capture the origins and the evolution of content within near-duplicate audio clusters, identifying the least altered versions and tracing the reuse of content within these clusters. The approach is evaluated for two selected application scenarios, demonstrating that it can accurately determine the direction of content reuse and identify parent-child relationships, while also offering a dedicated dataset for benchmarking future research in this area.

关键词： Directed acyclic graph computer vision conferences Media Benchmark testing Phylogeny pattern recognition

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Supervised deep learning of elastic SRV distances on the shape space of curves

Supervised deep learning of elastic SRV distances on the sha...

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

作者： Hartman, Emmanuel Sukurdeep, Yashil Charon, Nicolas Klassen, Eric Bauer, Martin Florida State Univ Dept Math Tallahassee FL 32306 USA Johns Hopkins Univ Dept Appl Math Baltimore MD USA

ISBN: (纸本)9781665448994

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, scalings, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments on both synthetic and real data.

关键词： Training Deep learning computer vision Shape conferences Transforms Tools

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Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

Data-Efficient Language-Supervised Zero-Shot Learning with S...

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

作者： Cheng, Ruizhe Wu, Bichen Zhang, Peizhao Vajda, Peter Gonzalez, Joseph E. Univ Calif Berkeley Berkeley CA 94720 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665448994

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use a simple pretraining task of predicting the pairings between images and text captions. CLIP, however, is data hungry and requires more than 400M image text pairs for training. We propose a data-efficient contrastive distillation method that uses soft labels to learn from noisy image-text pairs. Our model transfers knowledge from pre-trained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP. Our method exceeds the previous SoTA of general zero-shot learning on ImageNet 21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text encoder. We also beat CLIP by 10.5% relatively on zeroshot evaluation on Google Open Images (19,958 classes).

关键词： Training computer vision Visualization Natural languages Predictive models pattern recognition Internet

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