We propose a network flow based optimization method for data association needed multiple object tracking. The maximum-a-posteriori (MAP) problem is mapped into cost-flow with non-overlap constraint on trajectories. optimal found by min-cost algorithm in the network. augmented to include an Explicit Occlusion Model(EOM) track long-term inter-object occlusions. A solution EOM-based iterative approach built upon original algorithm. Initialization and termination of trajectories potential false...

The scene classification of high spatial resolution (HSR) images is a challenging task in the remote sensing community. How to construct discriminative representation HSR key step improve performance. In this letter, we propose novel feature extraction method termed multilayer fusion network (MF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) for classification. At first, transferred VGGNet-16 model employed as extractor acquire...

Semantic image segmentation, which aims at assigning pixel-wise category, is one of challenging understanding problems. Global context plays an important role on local category assignment. To make the best global context, in this paper, we propose dense relation network (DRN) and context-restricted loss (CRL) to aggregate information. DRN uses Recurrent Neural Network (RNN) with different skip lengths spatial directions get context-aware representations while CRL helps them learn...

In view of the low detection efficiency and high missed rate in current printed circuit board (PCB), this paper proposes an improved YOLOv3 PCB surface defect method. This method is based on network model. The improvement its structure mainly includes: 1. Combine batch normalization (BN, Batch Normalization) layer to convolutional layer, improve forward reasoning speed model, reduce model's defects training time dataset. 2. Aiming at problem that objective function evaluation metric are not...

The Large Vision-Language Model (LVLM) has enhanced the performance of various downstream tasks in visual-language understanding. Most existing approaches encode images and videos into separate feature spaces, which are then fed as inputs to large language models. However, due lack unified tokenization for videos, namely misalignment before projection, it becomes challenging a Language (LLM) learn multi-modal interactions from several poor projection layers. In this work, we unify visual...

Abstract Objective. Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endovascular therapy. Existing collateral scoring methods rely mainly on vessel segmentation and convolutional neural networks (CNNs), often ignoring bilateral brain differences. This study aims develop an automated model incorporating bilateral-difference awareness improve prediction accuracy. Approach. In this paper, we propose a new dual-branch hybrid network achieve...

Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by diffusion model, and then utilize feed-forward model to reconstruct content. However, often produce low-quality inconsistent images, adversely affecting the quality of final reconstruction. To address this issue, we propose unified generation framework called Cycle3D, which cyclically utilizes 2D diffusion-based module during multi-step process. Concretely, is applied for...

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, gains can be achieved by designing specifically segmentation, shown recent architecture search (NAS) research segmentation. One major challenge though is that ImageNet pre-training of space representation...

This paper presents a novel convolutional neural network (CNN)-based traffic sign recognition system and investigates pre- post-processing methods for enhancing performance. We focus on speed limit signs, the most difficult superclass in US set. The Cuda-convnet is chosen as suitable model task with low-resolution training images limited dataset size. test world's largest public of LISA-TS extension, testing dataset. Compared current state-of-the-art aggregated channel features detector that...

We use electronic Raman scattering to study the model single-layer cuprate superconductor HgBa(2)CuO(4+δ). In an overdoped sample, we observe a pronounced amplitude enhancement of high-energy peak related two-magnon excitations in insulating cuprates upon cooling below critical temperature T(c). This effect is accompanied by appearance superconducting gap and pairing above spectrum, it can be understood as hitherto-undetected feedback on magnetic fluctuations due Cooper interaction. implies...

Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks. However, both of them alone obvious shortcomings in practice. In this paper, we propose a novel end-to-end trainable deep neural network framework, named Pixel-Anchor, which combines SSD one feature sharing anchor-level attention mechanism to detect oriented text. To deal with has large variances size aspect ratio, combine FPN ASPP operation as our encoder-decoder...

This paper presents an effective single network for hand keypoint detection, instead of relying on the frequently-used two-stage pipeline consisting localizing and detecting key points. Our method trains a fully convolutional neural in end-to-end manner, based novelly proposed pose anchor network, which can be deemed as extension region proposal (RPN) Faster Region-based network. Moreover, we generate our data-driven way, i.e., K-means cluster algorithm object similarity (OKS), manually...

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, gains can be achieved by designing specifically segmentation, shown recent architecture search (NAS) research segmentation. One major challenge though, is that ImageNet pre-training of space representation...

Abstract To inhibit the destructive evolution of HfB 2 ‐SiC coating during oxidation, La O 3 was added to modify oxygen‐blocking capability coating. The effect content on oxygen barrier capacity investigated. addition 5 vol.% lowered oxidation activity and strengthened inert coating, which made permeability maximum weight change rate decrease by 30.32% 73.97%, respectively. Due solid solution reaction dispersed , HfO SiO nanoparticles, a stable Hf‐B‐La‐Si‐O multiphase glass formed surface...

Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, beneficial to bridging semantic gap between images natural language. Despite remarkable progress of deep learning detection recognition individual it still challenging localize recognize objects due complex combinatorial nature various kinds object relations. Inspired by recent advances one-shot learning, we propose simple yet effective Semantics Induced Learner...

Binocular disparity provides a powerful cue for depth perception in stereoscopic environment. Despite increasing knowledge of the cortical areas that process from neuroimaging studies, neural mechanism underlying sign processing [crossed (CD)/uncrossed (UD)] is still poorly understood. In present study, functional magnetic resonance imaging (fMRI) was used to explore different features are relevant disparity-sign processing.We performed an fMRI experiment on 27 right-handed healthy human...