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Next generation FPGAs and SOCs - How embedded systems can profit

Next generation FPGAs and SOCs - How embedded systems can pr...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Eberli, Felix Supercomp Syst AG CH-8005 Zurich Switzerland

ISBN: (纸本)9780769549903

New SOC like the Xilinx Zynq 7045 allow researchers and developers to combine the advantages of writing software for control functionality and having accelerators in the FPGA logic for the number crunching. The dual core Cortex-A9 ARM processor runs with up to 1 GHz and the FPGA has up to 900 DSP slices allowing a performance of up to 1,334 GMACs. SCS is porting a lot of algorithms like SGM stereo [1], Stixel clustering or an optical flow [2] to such devices allowing new cars to see their environment and react appropriately. The new developed SCS Zynq 7045 module will allow accelerated development using this technology.

关键词： digital signal processing chips embedded systems field programmable gate arrays image sequences pattern clustering stereo image processing system-on-chip

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Motion Fused Frames: Data Level Fusion Strategy for Hand Gesture recognition 31

Motion Fused Frames: Data Level Fusion Strategy for Hand Ges...

引用

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

作者： Kopuklu, Okan Kose, Neslihan Rigoll, Gerhard Tech Univ Munich Inst Human Machine Commun Munich Germany

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets -Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.

关键词： Optical imaging Feature extraction Gesture recognition Motion segmentation Optical sensors computer architecture Image color analysis

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An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

An Adversarial Approach for Explaining the Predictions of De...

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

作者： Rahnama, Arash Tseng, Andrew Modzy Vienna VA 22182 USA

ISBN: (纸本)9781665448994

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.

关键词： Deep learning computer vision Machine learning algorithms Computational modeling conferences Speech recognition Prediction algorithms

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Kernel ELM and CNN based Facial Age Estimation 29

Kernel ELM and CNN based Facial Age Estimation

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Gurpinar, Furkan Kaya, Heysem Dibeklioglu, Hamdi Salah, Albert Ali Bogazici Univ Program Computat Sci & Engn Istanbul Turkey Namik Kemal Univ Dept Comp Engn Tekirdag Turkey Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands Bogazici Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781509014378

e propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pre-trained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

关键词： Learning systems

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SIDOD: A Synthetic Image Dataset for 3D Object Pose recognition with Distractors 32

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognit...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jalal, Mona Spjut, Josef Boudaoud, Ben Betke, Margrit Boston Univ Boston MA 02215 USA NVIDIA Raleigh NC USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

关键词： Three-dimensional displays Pose estimation Cameras Image segmentation computer vision Training Lighting

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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Collective Activity Detection using Hinge-loss Markov Random Fields

Collective Activity Detection using Hinge-loss Markov Random...

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26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： London, Ben Khamis, Sameh Bach, Stephen H. Huang, Bert Getoor, Lise Davis, Larry Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9780769549903

We propose hinge-loss Markov random fields (HL-MRFs), a powerful class of continuous-valued graphical models, for high-level computer vision tasks. HL-MRFs are characterized by log-concave density functions, and are able to perform efficient, exact inference. Their templated hinge-loss potential functions naturally encode soft-valued logical rules. Using the declarative modeling language probabilistic soft logic, one can easily define HL-MRFs via familiar constructs from first-order logic. We apply HL-MRFs to the task of activity detection, using principles of collective classification. Our model is simple, intuitive and interpretable. We evaluate our model on two datasets and show that it achieves significant lift over the low-level detectors.

关键词： Markov processes computer vision image classification object detection probabilistic logic random processes

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Automatic Detection of Body Parts in X-Ray Images

Automatic Detection of Body Parts in X-Ray Images

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IEEE-computer-Society conference on computer vision and pattern recognition workshops

作者： Jeanne, Vincent Unay, Devrim Jacquet, Vincent Philips Res Labs Eindhoven Netherlands ISEN Brest France

ISBN: (纸本)9781424439942

The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technologic Accordingly medical image classification and retrieval has become a popular topic in the recent years. Despite many projects,focusing on this problem, proposed solutions are still far from being sufficiently accurate for real-life implementations. Interpreting medical image classification and retrieval as a multi-class classification task, in this work, we investigate the performance of five different feature types in a SVM-based learning framework-for classification of human body X-Ray images into classes corresponding to body parts. Our comprehensive experiments,show that four conventional feature types provide performances comparable to the literature with low per-class accuracies, whereas local binary patterns produce not only very good global accuracy but also good class-specific accuracies with respect to the features used in the literature.

关键词： Local binary pattern

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OutfitTransformer: Outfit Representations for Fashion Recommendation

OutfitTransformer: Outfit Representations for Fashion Recomm...

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

作者： Sarkar, Rohan Bodla, Navaneeth Vasileva, Mariya Lin, Yen-Liang Beniwal, Anurag Lu, Alan Medioni, Gerard Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, Out-fitTransformer, that learns compatibility of the entire out-fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.

关键词： computer vision conferences Task analysis Recommender systems pattern matching

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

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

作者： Lap Quoc Tran Huan Duc Vi Asilla Tokyo Japan

ISBN: (纸本)9798350365474

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision

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