亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

Selective robotic harvesting is a promising technological solution to address labour shortages which are affecting modern agriculture in many parts of the world. For an accurate and efficient picking process, a robotic harvester requires the precise location and orientation of the fruit to effectively plan the trajectory of the end effector. The current methods for estimating fruit orientation employ either complete 3D information which typically requires registration from multiple views or rely on fully-supervised learning techniques, which require difficult-to-obtain manual annotation of the reference orientation. In this paper, we introduce a novel key-point-based fruit orientation estimation method allowing for the prediction of 3D orientation from 2D images directly. The proposed technique can work without full 3D orientation annotations but can also exploit such information for improved accuracy. We evaluate our work on two separate datasets of strawberry images obtained from real-world data collection scenarios. Our proposed method achieves state-of-the-art performance with an average error as low as $8^{\circ}$, improving predictions by $\sim30\%$ compared to previous work presented in~\cite{wagner2021efficient}. Furthermore, our method is suited for real-time robotic applications with fast inference times of $\sim30$ms.

Deep neural networks (DNN) have achieved remarkable success in various fields, including computer vision and natural language processing. However, training an effective DNN model still poses challenges. This paper aims to propose a method to optimize the training effectiveness of DNN, with the goal of improving model performance. Firstly, based on the observation that the DNN parameters change in certain laws during training process, the potential of parameter prediction for improving model training efficiency and performance is discovered. Secondly, considering the magnitude of DNN model parameters, hardware limitations and characteristics of Stochastic Gradient Descent (SGD) for noise tolerance, a Parameter Linear Prediction (PLP) method is exploit to perform DNN parameter prediction. Finally, validations are carried out on some representative backbones. Experiment results show that compare to the normal training ways, under the same training conditions and epochs, by employing proposed PLP method, the optimal model is able to obtain average about 1% accuracy improvement and 0.01 top-1/top-5 error reduction for Vgg16, Resnet18 and GoogLeNet based on CIFAR-100 dataset, which shown the effectiveness of the proposed method on different DNN structures, and validated its capacity in enhancing DNN training efficiency and performance.

The impressive success of recent deep neural network (DNN)-based systems is significantly influenced by the high-quality datasets used in training. However, the effects of the datasets, especially how they interact with each other, remain underexplored. We propose a state-vector framework to enable rigorous studies in this direction. This framework uses idealized probing test results as the bases of a vector space. This framework allows us to quantify the effects of both standalone and interacting datasets. We show that the significant effects of some commonly-used language understanding datasets are characteristic and are concentrated on a few linguistic dimensions. Additionally, we observe some ``spill-over'' effects: the datasets could impact the models along dimensions that may seem unrelated to the intended tasks. Our state-vector framework paves the way for a systematic understanding of the dataset effects, a crucial component in responsible and robust model development.

Neural network generalizability is becoming a broad research field due to the increasing availability of datasets from different sources and for various tasks. This issue is even wider when processing medical data, where a lack of methodological standards causes large variations being provided by different imaging centers or acquired with various devices and cofactors. To overcome these limitations, we introduce a novel, generalizable, data- and task-agnostic framework able to extract salient features from medical images. The proposed quaternion wavelet network (QUAVE) can be easily integrated with any pre-existing medical image analysis or synthesis task, and it can be involved with real, quaternion, or hypercomplex-valued models, generalizing their adoption to single-channel data. QUAVE first extracts different sub-bands through the quaternion wavelet transform, resulting in both low-frequency/approximation bands and high-frequency/fine-grained features. Then, it weighs the most representative set of sub-bands to be involved as input to any other neural model for image processing, replacing standard data samples. We conduct an extensive experimental evaluation comprising different datasets, diverse image analysis, and synthesis tasks including reconstruction, segmentation, and modality translation. We also evaluate QUAVE in combination with both real and quaternion-valued models. Results demonstrate the effectiveness and the generalizability of the proposed framework that improves network performance while being flexible to be adopted in manifold scenarios.

Sixth-generation (6G) communication systems are poised to accommodate high data-rate wireless communication services in highly dynamic channels, with applications including high-speed trains, unmanned aerial vehicles, and intelligent transportation systems. Orthogonal frequency-division multiplexing (OFDM) modulation suffers from performance degradation in such high-mobility applications due to high Doppler spread in the channel. The recently proposed Orthogonal Time Frequency Space (OTFS) modulation scheme outperforms OFDM in terms of supporting a higher transmitter (Tx) and receiver (Rx) user velocity. Additionally, the highly-dynamic time-frequency (TF) channel has little effect on OTFS modulated signals, which enables the realization of low-complexity pre-processing architectures for implementing massive-multiple input multiple outputs (MIMO) based OTFS systems. However, while OTFS has received attention in the literature from a theory and simulation perspective, there has been comparatively little work on real-time FPGA implementation of OTFS waveforms. Thus, in this paper, we first present a mathematical overview of OTFS modulation and then describe an FPGA implementation of OTFS implementation on hardware. Power, area, and timing analysis of the implemented design on a Zynq UltraScale+ RFSoC FPGA are provided for benchmarking purposes.

This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad quality of services (QoS). In particular, these challenges hinder applications like intelligent transportation systems (ITS), metaverse, mixed reality, and the Internet of Everything, where real-time and efficient data transmission is paramount. Therefore, to reduce communication overhead and maintain the QoS of emerging applications such as metaverse, ITS, and digital twin creation, this work proposes a novel semantic communication framework. First, an intelligent semantic transmitter is designed to capture the meaningful information (e.g., the rode-side image in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel conditions. Finally, we have tested and evaluated the proposed semantic communication (SemCom) framework with the real-world 6G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate the efficacy of the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 93.45% data reduction in communication.

Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media platforms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and elevated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technologies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These advances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.

Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).

Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.