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KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

KiUT: Knowledge-injected U-Transformer for Radiology Report ...

引用

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

作者： Huang, Zhongzhen Zhang, Xiaofan Zhang, Shaoting Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively dis-till the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge.

关键词： cell microscopy Medical and biological vision

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A-CAP: Anticipation Captioning with Commonsense Knowledge

A-CAP: Anticipation Captioning with Commonsense Knowledge

引用

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

作者： Duc Minh Vo Quoc-An Luong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Grad Univ Adv Studies Hayama Kanagawa Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.

关键词： and reasoning language vision

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An Improved Association Pipeline for Multi-Person Tracking

An Improved Association Pipeline for Multi-Person Tracking

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Stadler, Daniel Beyerer, Jürgen Karlsruhe Institute of Technology Germany Fraunhofer IOSB Germany Fraunhofer Center for Machine Learning Germany

ISBN: (纸本)9798350302493

The association task of assigning detections to tracks in multi-person tracking has recently been improved by integration of a second matching stage for low-confident detections that are usually discarded in the tracking process. Despite its success, we find that this two stage matching has some weaknesses. For example, high-confident detections are preferred over low-confident detections in any case, even if the low-confident ones are more accurate. Therefore, a Combined Matching (CM) is proposed which considers all possible assignments simultaneously in a single matching stage and thus improves the association accuracy. Moreover, shortcomings of existing motion and appearance distance combinations are identified and a novel Combined Distance (CD) for motion and appearance information is introduced that significantly outperforms previous fusion approaches. Furthermore, we propose an Occlusion Aware Initialization (OAI) which prevents the start of ghost tracks from duplicate detections under occlusion. The effectiveness of our components is shown with extensive ablative experiments and the competitiveness of our tracker is demonstrated on the MOT17 and MOT20 benchmarks, where the current state-of-the-art is notably surpassed. © 2023 ieee.

关键词： computer vision

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deepPIC: Deep Perceptual Image Clustering For Identifying Bias In vision Datasets

deepPIC: Deep Perceptual Image Clustering For Identifying Bi...

引用

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

作者： Jaipuria, Nikita Stevo, Katherine Zhang, Xianling Gaopande, Meghana L. Garcia, Ian Calle Jain, Jinesh Murali, Vidya N. Ford Greenfield Labs Palo Alto CA 94304 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K;(ii) automatic nuanced metadata annotation;(iii) mining for edge cases;(iv) visualizing inherent bias both within and across multiple datasets;and (v) capturing synthetic data limitations;thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://***/unpuzzle_dataset_bias/. We recommend zooming in for best impact.

关键词： Analytical models computer vision Visualization Annotations Pipelines Buildings Data visualization

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Unmasking Your Expression: Expression-Conditioned GAN for Masked Face Inpainting

Unmasking Your Expression: Expression-Conditioned GAN for Ma...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Sola, Sridhar Gera, Darshan University of Birmingham Birmingham United Kingdom Sri Sathya Sai Institute of Higher Learning Bengaluru India

ISBN: (纸本)9798350302493

As face masks continue to be a part of our daily lives, the challenge of reconstructing occluded faces remains relevant. While several approaches have been proposed for removing masks from neutral facial images, few have explored the use of facial expressions as a dominant feature for reconstruction of expressive faces. To address this gap, we propose an expression-conditioned GAN (ECGAN) for reconstructing masked faces with a specified expression. Our approach leverages both the binary segmentation map of the mask and an expression label to generate high-quality images. To train our ECGAN in a supervised manner, we synthesize masked images using the RAFDB dataset to create non-masked-masked pairs of images for training. We evaluate of our approach on the RAFDB test set, demonstrating its effectiveness in generating realistic images that convincingly belong to the given expression class. This is further highlighted by comparing it to a baseline model and a state-of-the-art approach without expression-input. The code is available at https://***/SridharSola/ECGAN. © 2023 ieee.

关键词： computer vision

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Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

引用

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

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

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Learning to Ask Informative Sub-Questions for Visual Question Answering

Learning to Ask Informative Sub-Questions for Visual Questio...

引用

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

作者： Uehara, Kohei Duan, Nan Harada, Tatsuya Univ Tokyo Tokyo Japan Microsoft Res Asia Beijing Peoples R China Univ Tokyo RIKEN Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by in-putting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.

关键词： Training Visualization computer vision Computational modeling conferences Reinforcement learning Predictive models

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Does Federated Dropout actually work?

Does Federated Dropout actually work?

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

作者： Cheng, Gary Charles, Zachary Garrett, Zachary Rush, Keith Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Model sizes are limited in Federated Learning due to network bandwidth and on-device memory constraints. The success of increasing model sizes in other machine learning domains motivates the development of methods for training large-scale models in Federated Learning. To this end, [3] draws inspiration from dropout and proposes Federated Dropout: an algorithm where clients train randomly selected subsets of a larger server model. Despite the promising empirical results and the many other works that build on it [1, 8, 13], we argue in this paper that the metrics used to measure the performance of Federated Dropout and its variants are misleading. We propose and perform new experiments which suggest that Federated Dropout is actually detrimental to scaling efforts. We show how a simple ensembling technique outperforms Federated Dropout and other baselines. We perform ablations that suggest that the best performing variations of Federated Dropout approximate ensembling. The simplicity of ensembling allows for easy, practical implementations. Furthermore, ensembling naturally leverages the parallelizable nature of Federated Learning-recall that it is easy to train several models independently because there are a lot of clients and server-compute is not the bottleneck. Ensembling's strong performance against our baselines suggests that Federated Learning models may be more easily scaled than previously thought with more sophisticated ensembling strategies e.g., via boosting.

关键词： Training Measurement computer vision Machine learning algorithms conferences Computational modeling Memory management

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What Should Be Equivariant In Self-Supervised Learning

What Should Be Equivariant In Self-Supervised Learning

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

作者： Xie, Yuyang Wen, Jianhong Lau, Kin Wai Rehman, Yasar Abbas Ur Shen, Jiajun TCL AI Lab Hong Kong Peoples R China Fuzhou Univ Fuzhou Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Self-supervised learning (SSL) aims to learn feature representation without human-annotated data. Existing methods approach this goal by encouraging the feature representations to be invariant under a set of task-irrelevant transformations and distortions defined a priori. However, multiple studies have shown that such an assumption often limits the expressive power of the representations and model would perform poorly when downstream tasks violate this assumption. For example, being invariant to rotations would prevent features from retaining enough information to estimate object rotation angles. This suggests additional manual work and domain knowledge are required for selecting augmentation types during SSL. In this work, we relax the transformation-invariance assumption by introducing a SSL framework that encourages the feature representations to preserve the order of transformation scale in embedding space for some transformations while maintaining invariance to other transformations. This allows the learned feature representations to retain information about task-relevant transformations. In addition, this framework gives rise to a handy mechanism to determine the augmentation types to which the features representations should be invariant and equivariant during SSL. We demonstrate the effectiveness of our method on various datasets such as Fruits 360, Caltech-UCSD Birds 200, and Blood cells dataset.

关键词： computer vision conferences Self-supervised learning Cells (biology) Feature extraction Distortion Birds

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Efficient Two-stage Model Retraining for Machine Unlearning

Efficient Two-stage Model Retraining for Machine Unlearning

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

作者： Kim, Junyaup Woo, Simon S. PurpleChips Seoul South Korea Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the rise of the General Data Protection Regulation (GDPR), user data holders should guarantee the "individual's right to be forgotten". It means user data holders must completely remove user data when they receive the request. However, enabling a deep learning model to exclude specific data used during training is challenging. We cannot easily define the meaning of "forgetting" in deep learning and how to achieve it. To address this issue, we propose an efficient machine unlearning architecture to be used for computer vision classification models. Our approach consists of two-stage models, where in the first stage we enables a deep learning model that loses information with contrastive labels in the requested dataset. Second, we retrain the first stage output model with knowledge distillation (KD). Using this two-stage approach, we can substantiate the removal or forgetness of the requested dataset in the deep learning model. With various datasets used for multimedia applications, we demonstrate that our approach achieves performance on par or even higher accuracy than the original model, while effectively removing the requested data.

关键词： Deep learning Training computer vision Computational modeling conferences computer architecture Data models

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