The proceedings contain 143 papers. The topics discussed include: single-view obstacle detection for smart back-up camera systems;high-speed line-scan camera with multi-line CMOS color sensor;stereo vision embedded sy...
ISBN:
(纸本)9781467316118
The proceedings contain 143 papers. The topics discussed include: single-view obstacle detection for smart back-up camera systems;high-speed line-scan camera with multi-line CMOS color sensor;stereo vision embedded system for augmented reality;feature detection and matching on an SIMD/MIMD hybrid embedded processor;head-tracking virtual 3-D display for mobile devices;spatiotemporal multiple persons tracking using dynamic vision sensor;real-time body motion analysis for dance patternrecognition;embedded smart sensor for outdoor parking lot lighting control;embedded fall detection with a neural network and bio-inspired stereo vision;a GPU accelerated fast directional chamfer matching algorithm and a detailed comparison with a highly optimized CPU implementation;event-driven embodied system for feature extraction and object recognition in robotic applications;and a CPU-GPU hybrid people counting system for real-world airport scenarios using arbitrary oblique view cameras.
The proceedings contain 146 papers. The topics discussed include: textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion;a method for object localization in ...
ISBN:
(纸本)9781457705298
The proceedings contain 146 papers. The topics discussed include: textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion;a method for object localization in a multiview multimodal camera system;real-time tracking of unconstrained full-body motion using niching swarm filtering combined with local optimization;activity related biometric authentication using spherical harmonics;joint gait-pose manifold for video-based human motion estimation;learning human behaviour patterns in work environments;an optimized silicon retina stereo matching algorithm using time-space correlation;face recognition system using extended curvature Gabor classifier bunch for low-resolution face image;analysis of patterns of motor behavior in gamers with down syndrome;dense shape correspondences using spectral high-order graph matching;and pedestrian sensing using time-of-flight range camera.
The proceedings contain 162 papers. The topics discussed include: model-based validation approaches and matching techniques for automotive vision based pedestrian detection;3D face recognition using mapped depth image...
ISBN:
(纸本)0769526608
The proceedings contain 162 papers. The topics discussed include: model-based validation approaches and matching techniques for automotive vision based pedestrian detection;3D face recognition using mapped depth images;a combinational approach to the fusion, de-noising and enhancement of dual-energy x-ray luggage images;multiperspective thermal IR and video arrays for 3D body tracking and driver activity analysis;tracking humans using multi-modal fusion;improved likelihood function in particle-based IR eye tracking;spaceborne traffic monitoring with dual channel synthetic aperture radar – theory and experiments;comparative image fusion analysis;performance evaluation of face recognition using visual and thermal imagery with advanced correlation filters;and integrating LDV audio and ir video for remote multimodal surveillance.
The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computervision. This growth comes with rising demands for computatio...
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Model-based testing (MBT) allows a target software to be tested systematically and automatically by making use of a model of the software under test. It has been successfully applied in various domains. However its ap...
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Test flakiness, characterized by non-deterministic test outcomes despite unchanged code, poses a significant challenge to continuous integration system efficiency and developer productivity. We comprehensively study a...
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In this paper, we propose IRTR-DETR, an Interactive and Real-Time Rotated DEtection TRansformer that extends IRT-DETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTD...
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Automated test generation tools often produce assertions that reflect implemented behavior, limiting their usage to regression testing. In this paper, we propose LLMProphet, a black-box approach that applies Few-Shot ...
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We propose a method to display target colors along the trajectories of moving objects using high-speed projection. This is achieved by leveraging the selective disruption of the afterimage effect, ensuring the target ...
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The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computervision. This growth comes with rising demands for computatio...
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ISBN:
(数字)9798331536626
ISBN:
(纸本)9798331536633
The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computervision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR.
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