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Machine vision Applied to Backscattered Electron Imaging 6

Machine Vision Applied to Backscattered Electron Imaging

引用

6th ieee International conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Junyuan Zhang, Wei Chen, Jingyun Beihang University School of Mechanical Engineering and Automation Beijing China Beijing Institute of Space Launch Technology Beijing China

ISBN: (纸本)9798350325485

Electron beam processing usually needs to be carried out in a vacuum environment. The influence of high temperature and high concentration of metal vapor makes it difficult for traditional machine vision observation systems to perform long-Term observation. In this paper, a machine vision algorithm was used, a quadrupole plate sensor and image fusion algorithm were adopted, and the combination of software and hardware was realized. Finally, we solved the problems of scale imbalance and slice model scanning order in the imaging process and successfully realized clear observation of the surface in all directions. The backscattered electron signal imaging technology has been successfully applied to the electron beam processing process, which meets the online observation requirements of the electron beam processing process. © 2023 ieee.

关键词： computer vision

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Interventional Video Grounding with Dual Contrastive Learning

Interventional Video Grounding with Dual Contrastive Learnin...

引用

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

作者： Nan, Guoshun Qiao, Rui Xiao, Yao Liu, Jun Leng, Sicong Zhang, Hao Lu, Wei Singapore Univ Technol & Design StatNLP Res Grp Singapore Singapore Shanghai Jiao Tong Univ Shanghai Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781665445092

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y vertical bar X). Consequently, these models may suffer from spurious correlations between the language and video features due to the selection bias of the dataset. 1) To uncover the causality behind the model and data, we first propose a novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y vertical bar do(X)). Then, we present a simple yet effective method to approximate the unobserved confounder as it cannot be directly sampled from the dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations. Experiments on three standard benchmarks show the effectiveness of our approaches.

关键词： Visualization Correlation Grounding Benchmark testing Knowledge discovery Data models pattern recognition

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Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution

Learning Scene Structure Guidance via Cross-Task Knowledge T...

引用

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

作者： Sun, Baoli Ye, Xinchen Li, Baopu Li, Haojie Wang, Zhihui Xu, Rui Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training samples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected in actual testing environment. Therefore, we explore for the first time to learn the cross-modality knowledge at training stage, where both RGB and depth modalities are available, but test on the target dataset, where only single depth modality exists. Our key idea is to distill the knowledge of scene structural guidance from RGB modality to the single DSR task without changing its network architecture. Specifically, we construct an auxiliary depth estimation (DE) task that takes an RGB image as input to estimate a depth map, and train both DSR task and DE task collaboratively to boost the performance of DSR. Upon this, a cross-task interaction module is proposed to realize bilateral cross-task knowledge transfer. First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion. Then, we advance a structure prediction (SP) task that provides extra structure regularization to help both DSR and DE networks learn more informative structure representations for depth recovery. Extensive experiments demonstrate that our scheme achieves superior performance in comparison with other DSR methods.

关键词： Training Knowledge engineering Runtime Superresolution Network architecture pattern recognition Task analysis

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Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation

Alpha-Refine: Boosting Tracking Performance by Precise Bound...

引用

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

作者： Yan, Bin Zhang, Xinyu Wang, Dong Lu, Huchuan Yang, Xiaoyun Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Remark AI London England

ISBN: (纸本)9781665445092

Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage strategy to improve bounding box estimation. These methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. This work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base tracker's performance with little extra latency. The proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (AR strengthened DiMP50) achieve good efficiency-precision trade-off while the ARDiMPsuper (AR strengthened DiMPsuper) achieves very competitive performance at a realtime speed. Code and pretrained models are available at hups://***/MasterBin-IIALI/Alphalteline.

关键词： Couplings Visualization computer vision Target tracking Correlation Computational modeling Estimation

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Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation

Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dy...

引用

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

作者： Yao, Lewei Pi, Renjie Xu, Hang Zhang, Wei Li, Zhenguo Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead of naively pipelining these techniques, our Joint-DetNAS optimizes them jointly. The algorithm consists of two core processes: student morphism optimizes the student's architecture and removes the redundant parameters, while dynamic distillation aims to find the optimal matching teacher. For student morphism, weight inheritance strategy is adopted, allowing the student to flexibly update its architecture while fully utilize the predecessor's weights, which considerably accelerates the search;To facilitate dynamic distillation, an elastic teacher pool is trained via integrated progressive shrinking strategy, from which teacher detectors can be sampled without additional cost in subsequent searches. Given a base detector as the input, our algorithm directly outputs the derived student detector with high performance without additional training. Experiments demonstrate that our Joint-DetNAS outperforms the naive pipelining approach by a great margin. Given a classic R101-FPN as the base detector, Joint-DetNAS is able to boost its mAP from 41.4 to 43.9 on MS COCO and reduce the latency by 47%, which is on par with the SOTA EfficientDet while requiring less search cost. We hope our proposed method can provide the community with a new way of jointly optimizing NAS, KD and pruning.

关键词： Training Costs Heuristic algorithms Detectors Object detection computer architecture Search problems

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SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...

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

作者： Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.

关键词： Deep learning computer vision Convolution Computational modeling Semantics Benchmark testing Feature extraction

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Deep Dual Consecutive Network for Human Pose Estimation

Deep Dual Consecutive Network for Human Pose Estimation

引用

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

作者： Liu, Zhenguang Chen, Haoming Feng, Runyang Wu, Shuang Ji, Shouling Yang, Bailin Wang, Xun Zhejiang Gongshang Univ Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Multi-frame human pose estimation in complicated situations is challenging. Although state-of-the-art human joints detectors have demonstrated remarkable results for static images, their performances come short when we apply these models to video sequences. Prevalent shortcomings include the failure to handle motion blur, video defocus, or pose occlusions, arising from the inability in capturing the temporal dependency among video frames. On the other hand, directly employing conventional recurrent neural networks incurs empirical difficulties in modeling spatial contexts, especially for dealing with pose occlusions. In this paper, we propose a novel multi-frame human pose estimation framework, leveraging abundant temporal cues between video frames to facilitate keypoint detection. Three modular components are designed in our framework. A Pose Temporal Merger encodes keypoint spatiotemporal context to generate effective searching scopes while a Pose Residual Fusion module computes weighted pose residuals in dual directions. These are then processed via our Pose Correction Network for efficient refining of pose estimations. Our method ranks No.1 in the Multi-frame Person Pose Estimation Challenge on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018. We have released our code, hoping to inspire future research.

关键词： computer vision Recurrent neural networks Corporate acquisitions Pose estimation Video sequences Refining Detectors

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DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network

DivCo: Diverse Conditional Image Synthesis via Contrastive G...

引用

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

作者： Liu, Rui Ge, Yixiao Choi, Ching Lam Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China NVIDIA NVIDIA AI Technol Ctr Hong Kong Peoples R China Xidian Univ Sch CST Xian Peoples R China

ISBN: (纸本)9781665445092

Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. To solve this issue, previous works [47, 22] mainly focused on encouraging the correlation between the latent codes and their generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN [27] tried to encourage the diversity of the generated image but only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A novel latent-augmented contrastive loss is introduced, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent codes to be dissimilar. The proposed latent-augmented contrastive loss is well compatible with various cGAN architectures. Extensive experiments demonstrate that the proposed DivCo can produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple unpaired and paired image generation tasks. Training code and pretrained models are available at https://***/ruiliu-ai/DivCo.

关键词： Training Visualization computer vision Codes Correlation Image synthesis Generative adversarial networks

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Proceedings - 2022 ieee/cvf Winter conference on Applications of computer vision, WACV 2022

Proceedings - 2022 IEEE/CVF Winter Conference on Application...

引用

22nd ieee/cvf Winter conference on Applications of computer vision, WACV 2022

ISBN: (纸本)9781665409155

The proceedings contain 406 papers. The topics discussed include: evaluation of correctness in unsupervised many-to-many image translation;fast and explicit neural view synthesis;training a task-specific image reconstruction loss;single-photon camera guided extreme dynamic range imaging;extracting vignetting and grain filter effects from photos;high dynamic range imaging of dynamic scenes with saturation compensation but without explicit motion compensation;hers superpixels: deep affinity learning for hierarchical entropy rate segmentation;improving single-image defocus deblurring: how dual-pixel images help through multi-task learning;non-blind deblurring for fluorescence: a deformable latent space approach with kernel parameterization;and hierarchical modeling for task recognition and action segmentation in weakly-labeled instructional videos.

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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...

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

作者： Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of "labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

关键词： Training Adaptation models computer vision Training data Benchmark testing Data models Adversarial machine learning

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