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

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Partially Shared Multi-Task Convolutional Neural Network with Local Constraint for Face Attribute Learning 31

Partially Shared Multi-Task Convolutional Neural Network wit...

引用

31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cao, Jiajiong Li, Yingming Zhang, Zhongfei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

In this paper, we study the face attribute learning problem by considering the identity information and attribute relationships simultaneously. In particular, we first introduce a Partially Shared Multi-task Convolutional Neural Network (PS-MCNN), in which four Task Specific Networks (TSNets) and one Shared Network (SNet) are connected by Partially Shared (PS) structures to learn better shared and task specific representations. To utilize identity information to further boost the performance, we introduce a local learning constraint which minimizes the difference between the representations of each sample and its local geometric neighbours with the same identity. Consequently, we present a local constraint regularized multitask network, called Partially Shared Multi-task Convolutional Neural Network with Local Constraint (PS-MCNN-LC), where PS structure and local constraint are integrated together to help the framework learn better attribute representations. The experimental results on CelebA and LFWA demonstrate the promise of the proposed methods.

关键词： Task analysis Face Hair Convolutional neural networks computer architecture Feature extraction Bellows

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BACON: Band-limited Coordinate Networks for Multiscale Scene Representation

BACON: Band-limited Coordinate Networks for Multiscale Scene...

引用

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

作者： Lindell, David B. Van Veen, Dave Park, Jeong Joon Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Coordinate-based networks have emerged as a powerful tool for 3D representation and scene reconstruction. These networks are trained to map continuous input coordinates to the value of a signal at each point. Still, current architectures are black boxes: their spectral characteristics cannot be easily analyzed, and their behavior at unsupervised points is difficult to predict. Moreover, these networks are typically trained to represent a signal at a single scale, so naive downsampling or upsampling results in artifacts. We introduce band-limited coordinate networks (BACON), a network architecture with an analytical Fourier spectrum. BACON has constrained behavior at unsupervised points, can be designed based on the spectral characteristics of the represented signal, and can represent signals at multiple scales without per-scale supervision. We demonstrate BACON for multiscale neural representation of images, radiance fields, and 3D scenes using signed distance functions and show that it outperforms conventional single-scale co-ordinate networks in terms of interpretability and quality.

关键词： Time-frequency analysis Three-dimensional displays Fitting Bandwidth Network architecture Rendering (computer graphics) Behavioral sciences

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Human interaction recognition based on the co-occurrence of visual words

Human interaction recognition based on the co-occurrence of ...

引用

2014 IEEE conference on computer vision and pattern recognition Workshops, CVPRW 2014

作者： Slimani, Khadidja Nour El Houda Benezeth, Yannick Souami, Feriel USTHB LRIA Laboratory BP 32 El Alia Bab Ezzouar Algiers16111 Algeria Université de Bourgogne LE2I UMR CNRS 6306 Dijon cedex21000 France

ISBN: (纸本)9781479943098

This paper describes a novel methodology for automated recognition of high-level activities. A key aspect of our framework relies on the concept of co-occurring visual words for describing interactions between several persons. Motivated by the numerous success of human activity recognition methods using bag-of-words, this paradigm is extended. A 3-D XYT spatiooral volume is generated for each interacting person and a set of visual words is extracted to represent his activity. The interaction is then represented by the frequency of co-occurring visual words between persons. For our experiments, we used the UT-interaction dataset which contains several complex human-human interactions. © 2014 IEEE.

关键词： pattern recognition

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DSRNA: Differentiable Search of Robust Neural Architectures

DSRNA: Differentiable Search of Robust Neural Architectures

引用

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

作者： Hosseini, Ramtin Yang, Xingyi Xie, Pengtao Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture to change prediction outcomes significantly. To address this problem, we propose methods to perform differentiable search of robust neural architectures. In our methods, two differentiable metrics are defined to measure architectures' robustness, based on certified lower bound and Jacobian norm bound. Then we search for robust architectures by maximizing the robustness metrics. Different from previous approaches which aim to improve architectures' robustness in an implicit way: performing adversarial training and injecting random noise, our methods explicitly and directly maximize robustness metrics to harvest robust architectures. On CIFAR-10, ImageNet, and MNIST, we perform game-based evaluation and verification-based evaluation on the robustness of our methods. The experimental results show that our methods 1) are more robust to various norm-bound attacks than several robust NAS baselines;2) are more accurate than baselines when there are no attacks;3) have significantly higher certified lower bounds than baselines.

关键词： Measurement Jacobian matrices Deep learning Training Perturbation methods computer architecture Search problems

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Explicit Occlusion Modeling for 3D Object Class Representations

Explicit Occlusion Modeling for 3D Object Class Representati...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zia, M. Zeeshan Stark, Michael Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9780769549897

Despite the success of current state-of-the-art object class detectors, severe occlusion remains a major challenge. This is particularly true for more geometrically expressive 3D object class representations. While these representations have attracted renewed interest for precise object pose estimation, the focus has mostly been on rather clean datasets, where occlusion is not an issue. In this paper, we tackle the challenge of modeling occlusion in the context of a 3D geometric object class model that is capable of fine-grained, part-level 3D object reconstruction. Following the intuition that 3D modeling should facilitate occlusion reasoning, we design an explicit representation of likely geometric occlusion patterns. Robustness is achieved by pooling image evidence from of a set of fixed part detectors as well as a non-parametric representation of part configurations in the spirit of poselets. We confirm the potential of our method on cars in a newly collected data set of inner-city street scenes with varying levels of occlusion, and demonstrate superior performance in occlusion estimation and part localization, compared to baselines that are unaware of occlusions.

关键词： computer graphics image reconstruction image representation object detection pose estimation

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Are Multimodal Transformers Robust to Missing Modality?

Are Multimodal Transformers Robust to Missing Modality?

引用

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

作者： Ma, Mengmeng Ren, Jian Zhao, Long Testuggine, Davide Peng, Xi Univ Delaware Newark DE 19716 USA Snap Inc Santa Monica CA USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly preferred. Recently, Transformer models have shown great success in processing multimodal data. However, existing work has been limited to either architecture designs or pre-training strategies;whether Transformer models are naturally robust against missing-modal data has rarely been investigated. In this paper, we present the firstof-its-kind work to comprehensively investigate the behavior of Transformers in the presence of modal-incomplete data. Unsurprising, we find Transformer models are sensitive to missing modalities while different modal fusion strategies will significantly affect the robustness. What surprised us is that the optimal fusion strategy is dataset dependent even for the same Transformer model;there does not exist a universal strategy that works in general cases. Based on these findings, we propose a principle method to improve the robustness of Transformer models by automatically searching for an optimal fusion strategy regarding input data. Experimental validations on three benchmarks support the superior performance of the proposed method.

关键词： Training computer vision Benchmark testing Transformers Multitasking Search problems Data models

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Spatio-temporal Relation Modeling for Few-shot Action recognition

Spatio-temporal Relation Modeling for Few-shot Action Recogn...

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

作者： Thatipelli, Anirudh Narayan, Sanath Khan, Salman Anwer, Rao Muhammad Khan, Fahad Shahbaz Ghanem, Bernard Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Aalto Univ Espoo Finland Australian Natl Univ Canberra ACT Australia Linkoping Univ CVL Linkoping Sweden King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global frame-level enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a query-class similarity classifier on the patch-level enriched features to enhance class-specific feature discriminability by reinforcing the feature learning at different stages in the proposed framework. Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101. Our extensive ablation study reveals the benefits of the proposed contributions. Furthermore, our approach sets a new state-of-the-art on all four benchmarks. On the challenging SSv2 benchmark, our approach achieves an absolute gain of 3.5% in classification accuracy, as compared to the best existing method in the literature.

关键词： Representation learning computer vision Codes Computational modeling Aggregates Benchmark testing pattern recognition

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Layerwise Optimization by Gradient Decomposition for Continual Learning

Layerwise Optimization by Gradient Decomposition for Continu...

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

作者： Tang, Shixiang Chen, Dapeng Zhu, Jinguo Yu, Shijie Ouyang, Wanli Univ Sydney SenseTime Comp Vis Grp Sydney NSW Australia Xi An Jiao Tong Univ Xian Peoples R China Sensetime Grp Ltd Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Sydney NSW Australia

ISBN: (纸本)9781665445092

Deep neural networks achieve state-of-the-art and sometimes super-human performance across various domains. However, when learning tasks sequentially, the networks easily forget the knowledge of previous tasks, known as "catastrophic forgetting". To achieve the consistencies between the old tasks and the new task, one effective solution is to modify the gradient for update. Previous methods enforce independent gradient constraints for different tasks, while we consider these gradients contain complex information, and propose to leverage inter-task information by gradient decomposition. In particular, the gradient of an old task is decomposed into a part shared by all old tasks and a part specific to that task. The gradient for update should be close to the gradient of the new task, consistent with the gradients shared by all old tasks, and orthogonal to the space spanned by the gradients specific to the old tasks. In this way, our approach encourages common knowledge consolidation without impairing the task-specific knowledge. Furthermore, the optimization is performed for the gradients of each layer separately rather than the concatenation of all gradients as in previous works. This effectively avoids the influence of the magnitude variation of the gradients in different layers. Extensive experiments validate the effectiveness of both gradient-decomposed optimization and layerwise updates. Our proposed method achieves state-of-the-art results on various benchmarks of continual learning.

关键词： Knowledge engineering Deep learning computer vision Computational modeling Benchmark testing pattern recognition Task analysis

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Sequential Graph Convolutional Network for Active Learning

Sequential Graph Convolutional Network for Active Learning

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

作者： Caramalau, Razvan Bhattarai, Binod Kim, Tae-Kyun Imperial Coll London London England Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each images feature from a pool of data represents a node in the graph and the edges encode their similarities. With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes by minimising the binary cross-entropy loss. GCN performs message-passing operations between the nodes, and hence, induces similar representations of the strongly associated nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones. To this end, we utilise the graph node embeddings and their confidence scores and adapt sampling techniques such as CoreSet and uncertainty-based methods to query the nodes. We flip the label of newly queried nodes from unlabelled to labelled, re-train the learner to optimise the downstream task and the graph to minimise its modified objective. We continue this process within a fixed budget. We evaluate our method on 6 different benchmarks: 4 real image classification, 1 depth-based hand pose estimation and 1 synthetic RGB image classification datasets. Our method outperforms several competitive baselines such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art performance on multiple applications.

关键词： computer vision Convolution Image edge detection Pose estimation Benchmark testing pattern recognition Task analysis

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Edit One for All: Interactive Batch Image Editing

Edit One for All: Interactive Batch Image Editing

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

作者： Thao Nguyen Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae Univ Wisconsin Madison Madison WI 53707 USA

ISBN: (纸本)9798350353013;9798350353006

In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways;from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner. However, most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process, this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g., make the face frontal), our method can automatically transfer that edit to other test images, so that regardless of their initial state (pose), they all arrive at the same final state (e.g., all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods, while having more visual consistency and saving significant time and human effort.

关键词： batch image editing computer vision image editing image generation image manipulation

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