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

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Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation

Exploring and Distilling Posterior and Prior Knowledge for R...

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

作者： Liu, Fenglin Wu, Xian Ge, Shen Fan, Wei Zou, Yuexian Peking Univ Sch ECE ADSPLAB Beijing Peoples R China Tencent Med AI Lab Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Automatically generating radiology reports can improve current clinical practice in diagnostic radiology. On one hand, it can relieve radiologists from the heavy burden of report writing;On the other hand, it can remind radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis. Yet, this task remains a challenging job for data-driven neural networks, due to the serious visual and textual data biases. To this end, we propose a Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to imitate the working patterns of radiologists, who will first examine the abnormal regions and assign the disease topic tags to the abnormal regions, and then rely on the years of prior medical knowledge and prior working experience accumulations to write reports. Thus, the PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias;PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias. The explored knowledge is distilled by the MKD to generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our method is able to outperform previous state-of-the-art models on these two datasets.

关键词： Visualization computer vision MIMICs Neural networks Radiology Writing pattern recognition

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Learning Semantic Associations for Mirror Detection

Learning Semantic Associations for Mirror Detection

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

作者： Guan, Huankang Lin, Jiaying Lau, Rynson W. H. City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mirrors generally lack a consistent visual appearance, making mirror detection very challenging. Although recent works that are based on exploiting contextual contrasts and corresponding relations have achieved good results, heavily relying on contextual contrasts and corresponding relations to discover mirrors tend to fail in complex real-world scenes, where a lot of objects, e.g., doorways, may have similar features as mirrors. We observe that humans tend to place mirrors in relation to certain objects for specific functional purposes, e.g., a mirror above the sink. Inspired by this observation, we propose a model to exploit the semantic associations between the mirror and its surrounding objects for a reliable mirror localization. Our model first acquires class-specific knowledge of the surrounding objects via a semantic side-path. It then uses two novel modules to exploit semantic associations: 1) an Associations Exploration (AE) Module to extract the associations of the scene objects based on fully connected graph models, and 2) a Quadruple-Graph (QG) Module to facilitate the diffusion and aggregation of semantic association knowledge using graph convolutions. Extensive experiments show that our method outperforms the existing methods and sets the new state-of-the-art on both PMD dataset (f-measure: 0.844) and MSD daiaset (f-pleasure: 0.889). Code is available at https://***/guanhuankang/Learning-Semantic-Associations-for-Mirror-Detection.

关键词： Location awareness Convolutional codes Visualization computer vision Semantics pattern recognition Mirrors

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Dynamic Class Queue for Large Scale Face recognition In the Wild

Dynamic Class Queue for Large Scale Face Recognition In the ...

引用

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

作者： Li, Bi Xi, Teng Zhang, Gang Feng, Haocheng Han, Junyu Liu, Jingtuo Ding, Errui Liu, Wenyu Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on computing resource constraint and long-tailed class distribution. Recently, classification-based representation learning with deep neural networks and well-designed losses have demonstrated good recognition performance. However, the computing and memory cost linearly scales up to the number of identities (classes) in the training set, and the learning process suffers from unbalanced classes. In this work, we propose a dynamic class queue (DCQ) to tackle these two problems. Specifically, for each iteration during training, a subset of classes for recognition are dynamically selected and their class weights are dynamically generated on-the-fly which are stored in a queue. Since only a subset of classes is selected for each iteration, the computing requirement is reduced. By using a single server without model parallel, we empirically verify in large-scale datasets that 10% of classes are sufficient to achieve similar performance as using all classes. Moreover, the class weights are dynamically generated in a few-shot manner and therefore suitable for tail classes with only a few instances. We show clear improvement over a strong baseline in the largest public dataset Megaface Challenge2 (MF2) which has 672K identities and over 88% of them have less than 10 instances. Code is available at https ://***/bilylee/DCQ

关键词： Training Deep learning computer vision Costs Codes Face recognition Computational modeling

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Lepard: Learning partial point cloud matching in rigid and deformable scenes

Lepard: Learning partial point cloud matching in rigid and d...

引用

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

作者： Li, Yang Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Wako Saitama Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D position space. 2) A position encoding method that explicitly reveals 3D relative distance information through the dot product of vectors. 3) A repositioning technique that modifies the crosspoint-cloud relative positions. Ablation studies demonstrate the effectiveness of the above techniques. In rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark. Code and data are available at https://***/rabbityl/lepard.

关键词： Point cloud compression computer vision Three-dimensional displays Codes Art Motion segmentation Benchmark testing

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Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species

Few-shot Keypoint Detection with Uncertainty Learning for Un...

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

作者： Lu, Changsheng Koniusz, Piotr Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body parts, cannot recognize novel keypoints (keypoints not labelled for training) on unseen species. We raise an interesting yet challenging question: how to detect both base (annotated for training) and novel keypoints for unseen species given a few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection (FSKD) pipeline, which can detect a varying number of keypoints of different kinds. Our FSKD provides the uncertainty estimation of predicted keypoints. Specifically, FSKD involves main and auxiliary keypoint representation learning, similarity learning, and keypoint localization with uncertainty modeling to tackle the localization noise. Moreover, we model the uncertainty across groups of keypoints by multivariate Gaussian distribution to exploit implicit correlations between neighboring keypoints. We show the effectiveness of our FSKD on (i) novel keypoint detection for unseen species, (ii) few-shot Fine-Grained Visual recognition (FGVR) and (iii) Semantic Alignment (SA) downstream tasks. For FGVR, detected keypoints improve the classification accuracy. For SA, we showcase a novel thin-plate-spline warping that uses estimated keypoint uncertainty under imperfect keypoint corespondences.

关键词： Training Location awareness Representation learning computer vision Visualization Uncertainty Statistical analysis

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Collaborative and Adversarial Network for Unsupervised domain adaptation 31

Collaborative and Adversarial Network for Unsupervised domai...

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

作者： Zhang, Weichen Ouyang, Wanli Li, Wen Xu, Dong Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781538664209

In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain adversarial training of neural networks. We add several domain classifiers on multiple CNN feature extraction blocks1, in which each domain classifier is connected to the hidden representations from one block and one loss function is defined based on the hidden presentation and the domain labels (e.g., source and target). We design a new loss function by integrating the losses from all blocks in order to learn domain informative representations from lower blocks through collaborative learning and learn domain uninformative representations from higher blocks through adversarial learning. We further extend our CAN method as Incremental CAN (iCAN), in which we iteratively select a set of pseudo labelled target samples based on the image classifier and the last domain classifier from the previous training epoch and re-train our CAN model by using the enlarged training set. Comprehensive experiments on two benchmark datasets Office and ImageCLEF-DA clearly demonstrate the effectiveness of our newly proposed approaches CAN and iCAN for unsupervised domain adaptation.

关键词： Training Collaboration Feature extraction Adaptation models Visualization Task analysis computer vision

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A Closer Look at the Few-Shot Adaptation of Large vision-Language Models

A Closer Look at the Few-Shot Adaptation of Large Vision-Lan...

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

作者： Iguez, Julio Silva-Rodr Hajimiri, Sina Ben Ayed, Ismail Dolz, Jose ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350353006

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups, and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular, we make two interesting, and surprising empirical observations. First, to out-perform a simple Linear Probing baseline, these methods require to optimize their hyper-parameters on each target task. And second, they typically underperform -sometimes dramatically-standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature, i.e., access to a large validation set and case-specific grid-search for optimal hyperparameters, we propose a novel approach that meets the requirements of real-world scenarios. More concretely, we introduce a CLass-Adaptive linear Probe ( CLAP) objective, whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios, demonstrating that it consistently outperforms SoTA approaches, while yet being a much more efficient alternative. Code available at https://***/jusiro/CLAP.

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DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Mes...

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

作者： Liu, Minghua Sung, Minhyuk Mech, Radomir Su, Hao Univ Calif San Diego San Diego CA 92103 USA Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of metahandles for each shape, which are represented as combinations of the given handles. The disentangled meta-handles factorize all the plausible deformations of the shape, while each of them corresponds to an intuitive deformation. A new deformation can then be generated by sampling the coefficients of the meta-handles in a specific range. We employ biharmonic coordinates as the deformation function, which can smoothly propagate the control points' translations to the entire mesh. To avoid learning zero deformation as meta-handles, we incorporate a target-fitting module which deforms the input mesh to match a random target. To enhance deformations' plausibility, we employ a soft-rasterizer-based discriminator that projects the meshes to a 2D space. Our experiments demonstrate the superiority of the generated deformations as well as the interpretability and consistency of the learned meta-handles.

关键词： Deformable models Training Solid modeling computer vision Three-dimensional displays Shape Impedance matching

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Structured Multi-Level Interaction Network for Video Moment Localization via Language Query

Structured Multi-Level Interaction Network for Video Moment ...

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

作者： Wang, Hao Zha, Zheng-Jun Li, Liang Liu, Dong Luo, Jiebo Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment construction for both cross-modal understanding and video content comprehension, which are the two crucial challenges for this task. In this paper, we disentangle the activity moment into boundary and content. Based on the explored moment structure, we propose a novel Structured Multi-level Interaction Network (SMIN) to tackle this problem through multi-levels of cross-modal interaction coupled with content-boundary-moment interaction. In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interacting the word-level query with content and boundary, as in a coarse-to-fine manner. For content-boundary-moment interaction, we capture the insightful relations between boundary, content, and the whole moment proposal. Through multi-level interactions, the model obtains robust cross-modal representation for accurate moment localization. Extensive experiments conducted on three benchmarks (i.e., Charades-STA, ActivityNet-Captions, and TACoS) demonstrate the proposed approach outperforms the state-of-the-art methods.

关键词： Location awareness computer vision Natural languages Benchmark testing pattern recognition Proposals Task analysis

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DualGraph: A graph-based method for reasoning about label noise

DualGraph: A graph-based method for reasoning about label no...

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

作者： Zhang, HaiYang Xing, XiMing Liu, Liang Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

Unreliable labels derived from large-scale dataset prevent neural networks from fully exploring the data. Existing methods of learning with noisy labels primarily take noise-cleaning-based and sample-selection-based methods. However, for numerous studies on account of the above two views, selected samples cannot take full advantage of all data points and cannot represent actual distribution of categories, in particular if label annotation is corrupted. In this paper, we start from a different perspective and propose a robust learning algorithm called DualGraph, which aims to capture structural relations among labels at two different levels with graph neural networks including instance-level and distribution-level relations. Specifically, the instance-level relation utilizes instance similarity characterize sample category, while the distribution-level relation describes instance similarity distribution from each sample to all other samples. Since the distribution-level relation is robust to label noise, our network propagates it as supervised signals to refine instance-level similarity. Combining two level relations, we design an end-to-end training paradigm to counteract noisy labels while generating reliable predictions. We conduct extensive experiments on the noisy CIFAR-10 dataset, CIFAR-100 dataset, and the Clothing1M dataset. The results demonstrate the advantageous performance of the proposed method in comparison to state-of-the-art baselines.

关键词： Training computer vision Recurrent neural networks Reliability engineering Graph neural networks Robustness pattern recognition

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