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共 21008 条记录，以下是1521-1530 订阅

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Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning

Semiconductor Defect Detection by Hybrid Classical-Quantum D...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Yuan-Fu Sun, Min Natl Tsing Hua Univ 101Sect 2Kuang Fu Rd Hsinchu Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the development of new technology will bring many defect wafers. If these defect wafers have not been correctly inspected, the ineffective semiconductor processing on these defect wafers will cause additional impact to our environment, such as excessive carbon dioxide emission and energy consumption. In this paper, we utilize the information processing advantages of quantum computing to promote the defect learning defect review (DLDR). We propose a classical-quantum hybrid algorithm for deep learning on near-term quantum processors. By tuning parameters implemented on it, quantum circuit driven by our framework learns a given DLDR task, include of wafer defect map classification, defect pattern classification, and hotspot detection. In addition, we explore parametrized quantum circuits with different expressibility and entangling capacities. These results can be used to build a future roadmap to develop circuit-based quantum deep learning for semiconductor defect detection.

关键词： Deep learning Machine learning algorithms Program processors Qubit pattern classification Semiconductor device manufacture pattern recognition

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OW-DETR: Open-world Detection Transformer

OW-DETR: Open-world Detection Transformer

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gupta, Akshita Narayan, Sanath Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Shah, Mubarak Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates IIT Hyderabad Hyderabad India Australian Natl Univ Canberra ACT Australia Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden Univ Cent Florida Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-theart for all settings on PASCAL VOC. Our code is available at https://***/akshitac8/OW-DETR.

关键词： Training computer vision Object detection Transformers pattern recognition Proposals Object recognition

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Multi-Scale Memory-Based Video Deblurring

Multi-Scale Memory-Based Video Deblurring

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ji, Bo Yao, Angela Natl Univ Singapore Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video deblurring has achieved remarkable progress thanks to the success of deep neural networks. Most methods solve for the deblurring end-to-end with limited information propagation from the video sequence. However, different frame regions exhibit different characteristics and should be provided with corresponding relevant information. To achieve fine-grained deblurring, we designed a memory branch to memorize the blurry-sharp feature pairs in the memory bank, thus providing useful information for the blurry query input. To enrich the memory of our memory bank, we further designed a bidirectional recurrency and multi-scale strategy based on the memory bank. Experimental results demonstrate that our model outperforms other state-of-the-art methods while keeping the model complexity and inference time low The code is available at https://***/jibo27/MemDeblur.

关键词： Deep learning computer vision Runtime Costs Video sequences Neural networks Information retrieval

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AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks

AdaSTE: An Adaptive Straight-Through Estimator to Train Bina...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huu Le Hoier, Rasmus Kjaer Lin, Che-Tsung Zach, Christopher Chalmers Univ Technol Gothenburg Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bilevel optimization instance and subsequently construct flexible relaxations of this bilevel program. The resulting training method shares its algorithmic simplicity with several existing approaches to train BiNNs, in particular with the straight-through gradient estimator successfully employed in BinaryConnect and subsequent methods. In fact, our proposed method can be interpreted as an adaptive variant of the original straight-through estimator that conditionally (but not always) acts like a linear mapping in the backward pass of error propagation. Experimental results demonstrate that our new algorithm offers favorable performance compared to existing approaches.(1)

关键词： Training Deep learning computer vision Autonomous systems Neural networks Software algorithms Software

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Fisher Information Guidance for Learned Time-of-Flight Imaging

Fisher Information Guidance for Learned Time-of-Flight Imagi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Jiaqu Yue, Tao Zhao, Sijie Hu, Xuemei Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution. However, lower signal-to-noise ratio (SNR) of measurement leads to larger error in ToF imaging, especially for imaging scenes with strong ambient light or long distance. In this paper;we propose a Fisher-information guided framework to jointly optimize the coding functions (light modulation and sensor demodulation functions) and the reconstruction network of iToF imaging, with the super-vision of the proposed discriminative fisher loss. By introducing the differentiable modeling of physical imaging process considering various real factors and constraints, e.g., light-falloff with distance, physical implementability of coding functions, etc., followed by a dual-branch depth reconstruction neural network, the proposed method could learn the optimal iToF imaging system in an end-to-end manner. The effectiveness of the proposed method is extensively verified with both simulations and prototype experiments.

关键词： Training Photography Neural networks Imaging Prototypes Encoding pattern recognition

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RIO: Rotation-equivariance supervised learning of robust inertial odometry

RIO: Rotation-equivariance supervised learning of robust ine...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cao, Xiya Zhou, Caifa Zeng, Dandan Wang, Yongliang Huawei Technol Co Ltd Labs 2012 Riemann Lab Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models. We demonstrate that the self-supervised scheme provides a powerful supervisory signal at training phase as well as at inference stage. It reduces the reliance on massive amounts of labeled data for training a robust model and makes it possible to update the model using various unlabeled data. Further, we propose adaptive Test-Time Training (TTT) based on uncertainty estimations in order to enhance the generalizability of the inertial odometry to various unseen data. We show in experiments that the Rotation-equivariance-supervised Inertial Odometry (RIO) trained with 30% data achieves on par performance with a model trained with the whole dataset. Adaptive TTT improves models' performance in all cases and makes more than 25% improvements under several scenarios. We release our code and dataset at this website.

关键词： Training Adaptation models computer vision Uncertainty Codes Computational modeling Supervised learning

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RAMA: A Rapid Multicut Algorithm on GPU

RAMA: A Rapid Multicut Algorithm on GPU

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Abbas, Ahmed Swoboda, Paul Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a highly parallel primal-dual algorithm for the multicut (a.k.a. correlation clustering) problem, a classical graph clustering problem widely used in machine learning and computer vision. Our algorithm consists of three steps executed recursively: (1) Finding conflicted cycles that correspond to violated inequalities of the underlying multicut relaxation, (2) Performing message passing between the edges and cycles to optimize the Lagrange relaxation coming from the found violated cycles producing reduced costs and (3) Contracting edges with high reduced costs through matrix-matrix multiplications. Our algorithm produces primal solutions and lower bounds that estimate the distance to optimum. We implement our algorithm on GPUs and show resulting one to two orders-of-magnitudes improvements in execution speed without sacrificing solution quality compared to traditional sequential algorithms that run on CPUs. We can solve very large scale benchmark problems with up to O(10(8)) variables in a few seconds with small primal-dual gaps.

关键词： computer vision Machine learning algorithms Costs Tracking Shape Pose estimation Graphics processing units

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B-cos Networks: Alignment is All We Need for Interpretability

B-cos Networks: Alignment is All We Need for Interpretabilit...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Boehle, Moritz Fritz, Mario Schiele, Bernt MPI Informat Saarland Informat Campus Saarbrucken Germany CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace the linear transforms in DNNs by our Bcos transform. As we show, a sequence (network) of such transforms induces a single linear transform that faithfully summarises the full model computations. Moreover, the B-cos transform introduces alignment pressure on the weights during optimisation. As a result, those induced linear transforms become highly interpretable and align with task-relevant features. Importantly, the B-cos transform is designed to be compatible with existing architectures and we show that it can easily be integrated into common models such as VGGs, ResNets, InceptionNets, and DenseNets, whilst maintaining similar performance on ImageNet. The resulting explanations are of high visual quality and perform well under quantitative metrics for interpretability. Code available at ***/moboehle/B-cos.

关键词： Deep learning Training computer vision Visualization Computational modeling Neural networks Transforms

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Instance-wise Occlusion and Depth Orders in Natural Scenes

Instance-wise Occlusion and Depth Orders in Natural Scenes

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lee, Hyunmin Park, Jaesik LG AI Res Seoul South Korea POSTECH GSAI Pohang South Korea CSE Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the geometrical relationships of instances in an image. The dataset consists of 2.9M annotations of geometric orderings for class-labeled instances in 101K natural scenes. The scenes were annotated by 3,659 crowd-workers regarding (1) occlusion order that identifies occluder/occludee and (2) depth order that describes ordinal relations that consider relative distance from the camera. The dataset provides joint annotation of two kinds of orderings for the same instances, and we discover that the occlusion order and depth order are complementary. We also introduce a geometric order prediction network called InstaOrderNet, which is superior to state-of-the-art approaches. Moreover, we propose a dense depth prediction network called InstaDepthNet that uses auxiliary geometric order loss to boost the accuracy of the state-of-the-art depth prediction approach, MiDaS [54].

关键词： computer vision Annotations Cameras pattern recognition

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Do Explanations Explain? Model Knows Best

Do Explanations Explain? Model Knows Best

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Khakzar, Ashkan Khorsandi, Pedram Nobahari, Rozhin Navab, Nassir Tech Univ Munich Munich Germany Quebec Artificial Intelligence Inst Mila Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the problem. One peculiar observation is that these explanations (attributions) point to different features as being important. The phenomenon raises the question, which explanation to trust? We propose a framework for evaluating the explanations using the neural network model itself. The framework leverages the network to generate input features that impose a particular behavior on the output. Using the generated features, we devise controlled experimental setups to evaluate whether an explanation method conforms to an axiom. Thus we propose an empirical framework for axiomatic evaluation of explanation methods. We evaluate well-known and promising explanation solutions using the proposed framework. The framework provides a toolset to reveal properties and drawbacks within existing and future explanation solutions.

关键词： computer vision Perturbation methods Neural networks pattern recognition Behavioral sciences

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