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Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models

Bootstrapping Chest CT Image Understanding by Distilling Kno...

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

作者： Cao, Weiwei Zhang, Jianpeng Xia, Yingda Mok, Tony C. W. Li, Zi Ye, Xianghua Lu, Le Zheng, Jian Tang, Yuxing Zhang, Ling Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Beijing Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However, the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper, we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs, we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically, we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image, and perform pair-wise and semantic relation knowledge distillation. Subsequently, we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However, the challenge arises when patients have similar semantic diagnoses, such as healthy patients, potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios, including zero-shot learning, report generation, and fine-tuning processes, demonstrate the model's feasibility in interpreting chest CT images.

关键词： Medical vision language pre-training Report generation

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ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image

ANIM: Accurate Neural Implicit Model for Human Reconstructio...

引用

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

作者： Pesavento, Marco Xu, Yuanlu Sarafianos, Nikolaos Maier, Robert Wang, Ziyan Yao, Chun-Han Volino, Marco Boyer, Edmond Hilton, Adrian Tung, Tony Univ Surrey CVSSP Guildford Surrey England UC Merced Merced CA USA Meta Real Labs Sausalito CA USA

ISBN: (纸本)9798350353013;9798350353006

Recent progress in human shape learning, shows that neural implicit models are effective in generating 3D human surfaces from limited number of views, and even from a single RGB image. However, existing monocular approaches still struggle to recover fine geometric details such as face, hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships, mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy, which improves the accuracy of the signed distance field estimation of points that lie on the re-constructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data as input. In addi-tion, we introduce ANIM-Real, a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera, and our protocol to fine-tune ANIM, enabling high-quality reconstruction from real-world human capture. https://***/ANIM/

关键词： 3D Digital Avatars 3D Human reconstruction computer vision Neural Implicit Model

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Our Deep CNN Face Matchers Have Developed Achromatopsia

Our Deep CNN Face Matchers Have Developed Achromatopsia

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

作者： Bhatta, Aman Mery, Domingo Wu, Haiyu Annan, Joyce King, Michael C. Bowyer, Kevin W. Univ Notre Dame Notre Dame IN 46556 USA Pontificia Univ Catolica Chile Santiago Chile Florida Insitute Technol Melbourne FL USA FaceTec Las Vegas NV USA

ISBN: (纸本)9798350365474

Modern deep CNN face matchers are trained on datasets containing "color" images. We show that such matchers achieve essentially the same accuracy on color images when trained using only grayscale images. We then consider possible causes for deep CNN face matchers "not using color". Popular web-scraped face datasets actually have 30 to 60% of their identities with one or more grayscale images. We analyze whether this grayscale element in the training set impacts the accuracy achieved, and conclude that it does not. Comparable accuracy for color test images using only grayscale images implies that the inclusion of "color" may not necessarily add any significant information to the recognition of individuals. This also implies the use of computing resources can be optimized to make the training process more efficient using only grayscale images. Utilizing grayscale images for training reduces the memory footprint of the training data, thereby decreasing system processing time during training. Additionally, our findings emphasize that the adoption of grayscale images not only makes face recognition training more efficient but also offers the opportunity to include more training data, which could result in more accurate face recognition models.

关键词： Face recognition

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Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models

Generate Like Experts: Multi-Stage Font Generation by Incorp...

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

作者： Fu, Bin Yu, Fanghua Liu, Anran Wang, Zixuan Wen, Jie He, Junjun Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples, which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods, which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers, in this paper, we model font generation as a multi-stage generative process. Specifically, as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces, we are able to incorporate the font transfer process into these models. Based on this observation, we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image, and the font transfer stage subsequently transforms the source font to the target font. Finally, the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process, we construct our font generation framework, named MSD-Font, with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://***/fubinfb/MSD-Font

关键词： Diffusion Model Few-shot Font Generation Probabilistic Generative Model

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Unified Physical-Digital Attack Detection Challenge

Unified Physical-Digital Attack Detection Challenge

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

作者： Yuan, Haocheng Liu, Ajian Zheng, Junze Wan, Jun Deng, Jiankang Escalera, Sergio Escalante, Hugo Jair Guyon, Isabelle Lei, Zhen MUST Macau Peoples R China CASIA MAIS Beijing Peoples R China ICI PLC London England CVC Madrid Spain Chinese Acad Sci CAIR HKISI Beijing Peoples R China UCAS SAI Beijing Peoples R China CINVESTAV INAOE Mexico City DF Mexico ChaLearn Berkeley CA USA

ISBN: (纸本)9798350365474

Face Anti-Spoofing (FAS) is crucial to safeguard Face recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in realworld scenarios where FR systems face hybrid physical-digital threats. To facilitate the research of Unified Attack Detection (UAD) algorithms, a large-scale UniAttackData dataset has been collected. UniAttackData is the largest public dataset for Unified Attack Detection, with a total of 28,706 videos, where each unique identity encompasses all advanced attack types. Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections. It attracted 136 teams for the development phase, with 13 qualifying for the final round. The results re-verified by the organizing team were used for the final ranking. This paper comprehensively reviews the challenge, detailing the dataset introduction, protocol definition, evaluation criteria, and a summary of published results. Finally, we focus on the detailed analysis of the highest-performing algorithms and offer potential directions for unified physical-digital attack detection inspired by this competition. Challenge Website: https: // sites. google. com/ view/ face - anti - spoofing - challenge / welcome / challengecvpr2024

关键词： Face recognition

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

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

作者： Lee, Yi-Shan Tseng, Wei-Cheng Wang, Fu-En Sun, Min Natl Tsing Hua Univ Hsinchu Taiwan Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350365474

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer music

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Toward Stable, Interpretable, and Lightweight Hyperspectral Super-resolution

Toward Stable, Interpretable, and Lightweight Hyperspectral ...

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

作者： Guo, Wen-Jin Xie, Weiying Jiang, Kai Li, Yunsong Lei, Jie Fang, Leyuan Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9798350301298

For real applications, existing HSI-SR methods are not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance caused by the black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior into the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. Based on the spectral mixing prior, we then develop a partial fine-tune strategy to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against the state-of-the-arts under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://***/WenjinGuo/DAEM.

关键词： Low-level vision

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ViTs for SITS: vision Transformers for Satellite Image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time ...

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

作者： Tarasiou, Michail Chavez, Erik Zafeiriou, Stefanos Imperial Coll London London England

ISBN: (纸本)9798350301298

In this paper we introduce the Temporo-Spatial vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://***/michaeltrs/DeepSatModels.

关键词： Photogrammetry and remote sensing

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Content-aware Token Sharing for Efficient Semantic Segmentation with vision Transformers

Content-aware Token Sharing for Efficient Semantic Segmentat...

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

作者： Lu, Chenyang de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350301298

This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use vision Transformers (ViTs). Existing works have proposed token reduction approaches to improve the efficiency of ViT-based image classification networks, but these methods are not directly applicable to semantic segmentation, which we address in this work. We observe that, for semantic segmentation, multiple image patches can share a token if they contain the same semantic class, as they contain redundant information. Our approach leverages this by employing an efficient, class-agnostic policy network that predicts if image patches contain the same semantic class, and lets them share a token if they do. With experiments, we explore the critical design choices of CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes datasets, various ViT backbones, and different segmentation decoders. With Content-aware Token Sharing, we are able to reduce the number of processed tokens by up to 44%, without diminishing the segmentation quality.

关键词： Scene analysis and understanding

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Graph Representation for Order-aware Visual Transformation

Graph Representation for Order-aware Visual Transformation

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

作者： Qiu, Yue Sun, Yanjun Matsuzawa, Fumiya Iwata, Kenji Kataoka, Hirokatsu Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9798350301298

This paper proposes a new visual reasoning formulation that aims at discovering changes between image pairs and their temporal orders. Recognizing scene dynamics and their chronological orders is a fundamental aspect of human cognition. The aforementioned abilities make it possible to follow step-by-step instructions, reason about and analyze events, recognize abnormal dynamics, and restore scenes to their previous states. However, it remains unclear how well current AI systems perform in these capabilities. Although a series of studies have focused on identifying and describing changes from image pairs, they mainly consider those changes that occur synchronously, thus neglecting potential orders within those changes. To address the above issue, we first propose a visual transformation graph structure for conveying order-aware changes. Then, we bench-marked previous methods on our newly generated dataset and identified the issues of existing methods for change order recognition. Finally, we show a significant improvement in order-aware change recognition by introducing a new model that explicitly associates different changes and then identifies changes and their orders in a graph representation.

关键词： and reasoning language vision

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