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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Domain-robust VQA with diverse datasets and methods but no target labels

Domain-robust VQA with diverse datasets and methods but no t...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Mingda Maidment, Tristan Diab, Ahmad Kovashka, Adriana Hwa, Rebecca Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts. However, similar work on domain-robust visual question answering methods is very limited. Domain adaptation for VQA differs from adaptation for object recognition due to additional complexity: VQA models handle multimodal inputs, methods contain multiple steps with diverse modules resulting in complex optimization, and answer spaces in different datasets are vastly different. To tackle these challenges, we first quantify domain shifts between popular VQA datasets, in both visual and textual space. To disentangle shifts between datasets arising from different modalities, we also construct synthetic shifts in the image and question domains separately. Second, we test the robustness of different families of VQA methods (classic two-stream, transformer, and neuro-symbolic methods) to these shifts. Third, we test the applicability of existing domain adaptation methods and devise a new one to bridge VQA domain gaps, adjusted to specific VQA models. To emulate the setting of real-world generalization, we focus on unsupervised domain adaptation and the open-ended classification task formulation.

关键词： Visualization Adaptation models computer vision Computational modeling Semantics Linguistics Syntactics

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Consensus Maximisation Using Influences of Monotone Boolean Functions

Consensus Maximisation Using Influences of Monotone Boolean ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tennakoon, Ruwan Suter, David Zhang, Erchuan Chin, Tat-Jun Bab-Hadiashar, Alireza RMIT Univ Melbourne Vic Australia Edith Cowen Univ Perth WA Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.

关键词： computer vision Visualization Boolean functions Runtime Computational modeling Fitting Memory management

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An Approach to Recognize Efficient Deep Learning Model for pattern recognition

An Approach to Recognize Efficient Deep Learning Model for P...

引用

International conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Amandeep Kaur Monit Kapoor Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

pattern recognition using deep learning is one of the earliest and prevailing computer vision tasks and is a frequently used research area in today's world. Deep Learning methods are currently being used in numerous applications such as Video and speech recognition, computer vision tasks, cybernetics, text mining, financial fraud detection, etc. DL is derived from classical neural networks which improvises them extensively. Recently developed DL algorithms have achieved excellent performance in various applications mentioned above. Despite the successful use of deep learning models in numerous domains, constructing a suitable deep learning model remains a difficult job. This difficulty arises from the dynamic and diverse nature of real-world issues and inputs. This paper emphasizes recent developments in deep learning, types of DL techniques, its applications and further highlights the challenges and findings to ease researchers in identifying research needs in the area of pattern recognition.

关键词： Deep learning Text mining computer vision Visualization Computational modeling Speech recognition Artificial neural networks

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LQF: Linear Quadratic Fine-Tuning

LQF: Linear Quadratic Fine-Tuning

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Achille, Alessandro Golatkar, Aditya Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization. Such desirable properties are absent in deep neural networks (DNNs), typically trained by non-linear fine-tuning of a pre-trained model. Previous attempts to linearize DNNs have led to interesting theoretical insights, but have not impacted the practice due to the substantial performance gap compared to standard non-linear optimization. We present the first method for linearizing a pre-trained model that achieves comparable performance to non-linear fine-tuning on most of real-world image classification tasks tested, thus enjoying the interpretability of linear models without incurring punishing losses in performance. LQF consists of simple modifications to the architecture, loss function and optimization typically used for classification: Leaky-ReLU instead of ReLU, mean squared loss instead of cross-entropy, and pre-conditioning using Kronecker factorization. None of these changes in isolation is sufficient to approach the performance of non-linear fine-tuning. When used in combination, they allow us to reach comparable performance, and even superior in the low-data regime, while enjoying the simplicity, robustness and interpretability of linear-quadratic optimization.

关键词： Deep learning computer vision Training data computer architecture Robustness pattern recognition Task analysis

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Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Self-Supervised Simultaneous Multi-Step Prediction of Road D...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Amirloo, Elmira Rohani, Mohsen Banijamali, Ershad Luo, Jun Poupart, Pascal Huawei Noahs Ark Lab Toronto ON Canada Univ Waterloo Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781665445092

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In contrast, while end-to-end architectures do not require labeled data and are potentially more scalable, interpretability is sacrificed. We introduce a novel architecture that is trained in a fully self-supervised fashion for simultaneous multi-step prediction of space-time cost map and road dynamics. Our solution replaces the manually designed cost function for motion planning with a learned high dimensional cost map that is naturally interpretable and allows diverse contextual information to be integrated without manual data labeling. Experiments on real world driving data show that our solution leads to lower number of collisions and road violations in long planning horizons in comparison to baselines, demonstrating the feasibility of fully self-supervised prediction without sacrificing scalability.

关键词： Costs Roads Scalability Supervised learning Dynamics computer architecture Manuals

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CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNet V2: Sparse Feature Reactivation for Deep Network...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Le Jiang, Haojun Cai, Ruojin Wang, Yulin Song, Shiji Huang, Gao Tian, Qi Tsinghua Univ Dept Automat Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Cornell Univ Ithaca NY 14853 USA Huawei Cloud & AI Ithaca NY USA

ISBN: (纸本)9781665445092

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet [14] has shown that this mechanism can befiirther improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers;and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.

关键词： Training computer vision Computational modeling Object detection computer architecture Search problems pattern recognition

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Single image HDR synthesis using a Densely Connected Dilated ConvNet

Single image HDR synthesis using a Densely Connected Dilated...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akhil, K. A. Jiji, C., V Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781665448994

Visual representations using high dynamic range (HDR) images have become increasingly popular because of their high quality and expressive ability. HDR images are expected to be used in a broad range of applications, including digital cinema, photography, and broadcast. The generation of a HDR image from a single exposure Low Dynamic Range (LDR) image is a challenging task where one must make up for missing data due to underexposure or overexposure and color quantization. In this paper, we propose a deep convolutional neural network (CNN) model with a stack of dilated convolutional blocks for reconstructing a HDR image from a single LDR image. Within each dilation block, the dilation rate of the convolution layer is three and progressively decreases to one. Multiple dilation convolution blocks are further connected densely to improve the representation capacity of the network. As the network is trained in a supervised manner, the additional information is reconstructed from learned features. Our experimental results show that the model effectively captures missing information that was lost from the original image.

关键词： Photography Visualization Quantization (signal) Convolution Dynamic range pattern recognition Convolutional neural networks

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Anshul Gupta Pierre Vuillecard Arya Farkhondeh Jean-Marc Odobez Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Contextual cues related to a person’s pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Visualization computer vision conferences Computational modeling Predictive models pattern recognition Context modeling

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AdaBins: Depth Estimation Using Adaptive Bins

AdaBins: Depth Estimation Using Adaptive Bins

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bhat, Shariq Farooq Alhashim, Ibraheem Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.

关键词： Measurement Image segmentation Three-dimensional displays Image resolution Estimation computer architecture Transformers

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4D Hyperspectral Photoacoustic Data Restoration with Reliability Analysis

4D Hyperspectral Photoacoustic Data Restoration with Reliabi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liao, Weihang Subpa-Asa, Art Zheng, Yinqiang Sato, Imari Tokyo Inst Technol Tokyo Japan Natl Inst Informat Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains;and the acquired data tend to suffer from complex noise. This time-consuming scanning process and noise severely affects the usability of HSPA. It is therefore critical to examine the feasibility of 4D HSPA data restoration from an incomplete and noisy observation. In this work, we present a data reliability analysis for the depth and spectral domain. On the basis of this analysis, we explore the inherent data correlations and develop a restoration algorithm to recover 4D HSPA cubes. Experiments on real data verify that the proposed method achieves satisfactory restoration results.

关键词： Legged locomotion Spectroscopy Correlation Three-dimensional displays pattern recognition Reliability Noise measurement

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