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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Deep Learning for Automatic Pneumonia Detection

Deep Learning for Automatic Pneumonia Detection

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gabruseva, Tatiana Poplavskiy, Dmytro Kalinin, Alexandr Topcon Positioning Syst Brisbane Qld Australia Univ Michigan Ann Arbor MI 48109 USA Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9781728193601

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-Ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-extinction deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.

关键词： computer aided diagnosis

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On the Stability-Plasticity Dilemma of Class-Incremental Learning

On the Stability-Plasticity Dilemma of Class-Incremental Lea...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dongwan Kim Bohyung Han Computer Vision Laboratory ECE Seoul National University IPAI Seoul National University

A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from previously seen classes, and plastic enough to learn concepts from new classes. While previous works demonstrate strong performance on class-incremental benchmarks, it is not clear whether their success comes from the models being stable, plastic, or a mixture of both. This paper aims to shed light on how effectively recent class-incremental learning algorithms address the stability-plasticity trade-off. We establish analytical tools that measure the stability and plasticity of feature representations, and employ such tools to investigate models trained with various algorithms on large-scale class-incremental benchmarks. Surprisingly, we find that the majority of class-incremental learning algorithms heavily favor stability over plasticity, to the extent that the feature extractor of a model trained on the initial set of classes is no less effective than that of the final incremental model. Our observations not only inspire two simple algorithms that highlight the importance of feature representation analysis, but also suggest that class-incremental learning approaches, in general, should strive for better feature representation learning.

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Neurodata Lab's approach to the Challenge on computer vision for Physiological Measurement

Neurodata Lab's approach to the Challenge on Computer Vision...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Artemyev, Mikhail Churikova, Marina Grinenko, Mikhail Perepelkina, Olga Neurodata Lab LLC Miami FL 33137 USA Lomonosov Moscow State Univ Fac Biol Dept Higher Nervous Act Moscow Russia

ISBN: (纸本)9781728193601

This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within CVPR2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision. Our deep learning-based approach includes 3D spatio-temporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).

关键词： computer vision

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Comprehensive and Delicate: An Efficient Transformer for Image Restoration

Comprehensive and Delicate: An Efficient Transformer for Ima...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haiyu Zhao Yuanbiao Gou Boyun Li Dezhong Peng Jiancheng Lv Xi Peng College of Computer Science Sichuan University

vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations. Although the promising performance has been achieved, they go against the biggest success factor of Transformers to a certain extent by capturing the local instead of global de-pendency among pixels. In this paper, we propose a novel efficient image restoration Transformer that first captures the superpixel-wise global dependency, and then transfers it into each pixel. Such a coarse-to-fine paradigm is implemented through two neural blocks, i.e., condensed attention neural block (CA) and dual adaptive neural block (DA). In brief, CA employs feature aggregation, attention computation, and feature recovery to efficiently capture the global dependency at the superpixel level. To embrace the pixel-wise global dependency, DA takes a novel dual-way structure to adaptively encapsulate the globality from superpix-els into pixels. Thanks to the two neural blocks, our method achieves comparable performance while taking only ~6% FLOPs compared with SwinIR.

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

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conference on computer vision and pattern recognition (CVPR)

作者： Paul Hongsuck Seo Arsha Nagrani Cordelia Schmid Google Research

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively; and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

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Learning Conditional Attributes for Compositional Zero-Shot Learning

Learning Conditional Attributes for Compositional Zero-Shot ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Qingsheng Wang Lingqiao Liu Chenchen Jing Hao Chen Guoqiang Liang Peng Wang Chunhua Shen School of Computer Science and Ningbo Institute Northwestern Polytechnical University Xi'an China School of Computer Science University of Adelaide Adelaide Australia School of Computer Science Zhejiang University Hangzhou China

Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute “wet” in “wet apple” and “wet cat” is different. As a solution, we provide analysis and argue that attributes are conditioned on the recognized object and input image and explore learning conditional attribute embeddings by a proposed attribute learning framework containing an attribute hyper learner and an attribute base learner. By encoding conditional attributes, our model enables to generate flexible attribute embeddings for generalization from seen to unseen compositions. Experiments on CZSL benchmarks, including the more challenging C-GQA dataset, demonstrate better performances compared with other state-of-the-art approaches and validate the importance of learning conditional attributes. Code ‡ 1 Gllee:https://***/wqshmzh/canet-czsl is available at https://***/wqshmzh/CANet-CZSL.

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Contrastive Grouping with Transformer for Referring Image Segmentation

Contrastive Grouping with Transformer for Referring Image Se...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jiajin Tang Ge Zheng Cheng Shi Sibei Yang School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging

Referring image segmentation aims to segment the target referent in an image conditioning on a natural language expression. Existing one-stage methods employ per-pixel classification frameworks, which attempt straightforwardly to align vision and language at the pixel level, thus failing to capture critical object-level information. In this paper, we propose a mask classification framework, Contrastive Grouping with Transformer network (CGFormer), which explicitly captures object-level information via token-based querying and grouping strategy. Specifically, CGFormer first introduces learnable query tokens to represent objects and then alternately queries linguistic features and groups visual features into the query tokens for object-aware cross-modal reasoning. In addition, CGFormer achieves cross-level interaction by jointly updating the query tokens and decoding masks in every two consecutive layers. Finally, CGFormer cooperates contrastive learning to the grouping strategy to identify the token and its mask corresponding to the referent. Experimental results demonstrate that CG-Former outperforms state-of-the-art methods in both segmentation and generalization settings consistently and significantly. Code is available at https://***/Toneyaya/CGFormer.

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Document Image Shadow Removal Guided by Color-Aware Background

Document Image Shadow Removal Guided by Color-Aware Backgrou...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ling Zhang Yinghao He Qing Zhang Zheng Liu Xiaolong Zhang Chunxia Xiao School of Computer Science and Technology Wuhan University of Science and Technology School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science China University of Geosciences (Wuhan) School of Computer Science Wuhan University

Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document. Furthermore, we propose a background-guided document images shadow removal network (BGShadowNet) using the predicted spatially varying background as auxiliary information, which consists of two stages. At Stage I, a background-constrained decoder is designed to promote a coarse result. Then, the coarse result is refined with a background-based attention module (BAModule) to maintain a consistent appearance and a detail improvement module (DEModule) to enhance the texture details at Stage II. Experiments on two benchmark datasets qualitatively and quantitatively validate the superiority of the proposed approach over state-of-the-arts.

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MAP: Multimodal Uncertainty-Aware vision-Language Pre-training Model

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-traini...

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conference on computer vision and pattern recognition (CVPR)

作者： Yatai Ji Junjie Wang Yuan Gong Lin Zhang Yanru Zhu Hongfa Wang Jiaxing Zhang Tetsuya Sakai Yujiu Yang Tsinghua University Waseda University IDEA Tencent TEG

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pretraining on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pretraining frameworks and propose suitable pretraining tasks: Distribution-based vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Daniel Bogdoll Maximilian Nitsche J. Marius Zö llner FZI Research Center for Information Technology Karlsruhe Germany KIT Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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