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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21006 条记录，以下是4931-4940 订阅

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Connecting What to Say With Where to Look by Modeling Human Attention Traces

Connecting What to Say With Where to Look by Modeling Human ...

引用

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

作者： Meng, Zihang Yu, Licheng Zhang, Ning Berg, Tamara Damavandi, Babak Singh, Vikas Bearman, Amy Univ Wisconsin Madison WI 53705 USA Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [31], where each word of a given caption is paired with a mouse trace segment. We propose two novel tasks: (1) predict a trace given an image and caption (i.e., visual grounding), and (2) predict a caption and a trace given only an image. Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded. We present a novel model architecture that is jointly trained on dual tasks (controlled trace generation and controlled caption generation). To evaluate the quality of the generated traces, we propose a local bipartite matching (LBM) distance metric which allows the comparison of two traces of different lengths. Extensive experiments show our model is robust to the imperfect training data and outperforms the baselines by a clear margin. Moreover, we demonstrate that our model pre-trained on the proposed tasks can be also beneficial to the downstream task of COCO's guided image captioning. Our code(1) and project page2 are publicly available.

关键词： Measurement Visualization Grounding Unified modeling language Training data computer architecture Transformers

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Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

Contrastive Learning based Hybrid Networks for Long-Tailed I...

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

作者： Wang, Peng Han, Kai Wei, Xiu-Shen Zhang, Lei Wang, Lei Univ Wollongong Wollongong NSW Australia Univ Bristol Bristol Avon England Nanjing Univ Sci & Technol Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification.

关键词： Training computer vision Memory management Graphics processing units Image representation pattern recognition Proposals

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MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

引用

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Mei, Tao JD Explore Acad Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data. In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (i.e., token-mixing MLP) and one MLP applied independently to each token (i.e., channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search. Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5%/81.4% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers.

关键词： Visualization computer vision Three-dimensional displays computer architecture Transformers pattern recognition Complexity theory

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FGN: Fully Guided Network for Few-Shot Instance Segmentation

FGN: Fully Guided Network for Few-Shot Instance Segmentation

引用

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

作者： Fan, Zhibo Yu, Jin-Gang Liang, Zhihao Ou, Jiarong Gao, Changxin Xia, Gui-Song Li, Yuanqing South China Univ Technol Guangzhou Peoples R China Guangzhou Lab Guangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781728171685

Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training. This paper presents a Fully Guided Network (FGN) for few-shot instance segmentation. FGN perceives FSIS as a guided model where a so-called support set is encoded and utilized to guide the predictions of a base instance segmentation network (i.e., Mask R-CNN), critical to which is the guidance mechanism. In this view, FGN introduces different guidance mechanisms into the various key components in Mask R-CNN, including Attention-Guided RPN, Relation-Guided Detector, and Attention-Guided FCN, in order to make full use of the guidance effect from the support set and adapt better to the inter-class generalization. Experiments on public datasets demonstrate that our proposed FGN can outperform the state-of-the-art methods.

关键词： computer vision

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Sonar Automatic Target recognition for Underwater UXO Remediation

Sonar Automatic Target Recognition for Underwater UXO Remedi...

引用

ieee conference on computer vision and pattern recognition (cvpr)

作者： Isaacs, Jason C. Naval Surface Warfare Ctr Panama City FL 32407 USA

ISBN: (纸本)9781467367592

Automatic target recognition (ATR) for unexploded ordnance (UXO) detection and classification using sonar data of opportunity from open oceans survey sites is an open research area. The goal here is to develop ATR spanning real-aperture and synthetic aperture sonar imagery. The classical paradigm of anomaly detection in images breaks down in cluttered and noisy environments. In this work we present an upgraded and ultimately more robust approach to object detection and classification in image sensor data. In this approach, object detection is performed using an in situ weighted highlight-shadow detector: features are generated on geometric moments in the imaging domain: and finally, classification is performed using an Ada-boosted decision tree classifier These techniques are demonstrated on simulated real aperture sonar data with varying noise levels.

关键词： Apertures Feature extraction Sonar detection Sonar navigation Synthetic aperture sonar Training

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Camouflaged Object Segmentation with Distraction Mining

Camouflaged Object Segmentation with Distraction Mining

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

作者： Mei, Haiyang Ji, Ge-Peng Wei, Ziqi Yang, Xin Wei, Xiaopeng Fan, Deng-Ping Dalian Univ Technol Dalian Peoples R China Wuhan Univ Wuhan Peoples R China Tsinghua Univ Beijing Peoples R China IIAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Specifically, our PFNet contains two key modules, i.e., the positioning module (PM) and the focus module (FM). The PM is designed to mimic the detection process in predation for positioning the potential target objects from a global perspective and the FM is then used to perform the identification process in predation for progressively refining the coarse prediction via focusing on the ambiguous regions. Notably, in the FM, we develop a novel distraction mining strategy for the distraction discovery and removal, to benefit the performance of estimation. Extensive experiments demonstrate that our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets under four standard metrics.

关键词： Measurement Frequency modulation MIMICs Refining Object segmentation Real-time systems pattern recognition

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Effective Snapshot Compressive-spectral Imaging via Deep Denoising and Total Variation Priors

Effective Snapshot Compressive-spectral Imaging via Deep Den...

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

作者： Qiu, Haiquan Wang, Yao Meng, Deyu Xi An Jiao Tong Univ Xian Peoples R China Shanghai Data Technol Co Ltd Shanghai Peoples R China Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9781665445092

Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-and-play framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-and-play algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://***/ucker/SCI-TVFFDNet.

关键词： Image coding TV Noise reduction Imaging Sensors pattern recognition Optimization

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SMD-Nets: Stereo Mixture Density Networks

SMD-Nets: Stereo Mixture Density Networks

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

作者： Tosi, Fabio Liao, Yiyi Schmitt, Carolin Geiger, Andreas Univ Bologna Dept Comp Sci & Engn DISI Bologna Italy Univ Tubingen Autonomous Vis Grp MPI IS Tubingen Germany

ISBN: (纸本)9781665445092

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.

关键词： Solid modeling Three-dimensional displays Uncertainty Superresolution Estimation pattern recognition Spatial resolution

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Simulated Adversarial Testing of Face recognition Models

Simulated Adversarial Testing of Face Recognition Models

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

作者： Ruiz, Nataniel Kortylewski, Adam Qiu, Weichao Xie, Cihang Bargal, Sarah Adel Yuille, Alan Sclaroff, Stan Boston Univ Boston MA 02215 USA Johns Hopkins Univ Baltimore MD 21218 USA Huawei Shenzhen Peoples R China UC Santa Cruz Santa Cruz Do Sul RS Brazil

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be revealed at test time in the real world. The risks involved in such failures can be loss of profits, loss of time or even loss of life in certain critical applications. In order to alleviate this issue, simulators can be controlled in a fine-grained manner using interpretable parameters to explore the semantic image manifold. In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios. We apply this method in a face recognition setup. We show that certain weaknesses of models trained on real data can be discovered using simulated samples. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected spaces in the latent space of the simulator. We present a method to find these adversarial regions as opposed to the typical adversarial points found in the adversarial example literature.

关键词： Training Manifolds Shape Face recognition Semantics Nose Machine learning

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Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

Strip Pooling: Rethinking Spatial Pooling for Scene Parsing

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

作者： Hou, Qibin Zhang, Li Cheng, Ming-Ming Feng, Jiashi Natl Univ Singapore Singapore Singapore Univ Oxford Oxford England Nankai Univ CS Tianjin Peoples R China

ISBN: (纸本)9781728171685

Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of N x N, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1 x N or N x 1. Based on strip pooling, we further investigate spatial pooling architecture design by I) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies, 2) presenting a novel building block with diverse spatial pooling as a core, and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plugand-play module in existing scene parsing networks. Extensive experiments on popular benchmarks (e.g., ADE20K and Cityscapes) demonstrate that our simple approach establishes new state-of-the-art results. Code is available at https://***/Andrew-Qibin/SPNet.

关键词： computer vision

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