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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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Momentum Contrastive Pruning

Momentum Contrastive Pruning

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

作者： Pan, Siyuan Qin, Yiming Li, Tingyao Li, Xiaoshuang Hou, Liang Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Momentum contrast [16] (MoCo) for unsupervised visual representation learning has a close performance to supervised learning, but it sometimes possesses excess parameters. Extracting a subnetwork from an over-parameterized unsupervised network without sacrificing performance is of particular interest to accelerate inference speed. Typical pruning methods are not applicable for MoCo, because in the fine-tune stage after pruning, the slow update of the momentum encoder will undermine the pretrained encoder. In this paper, we propose a Momentum Contrastive Pruning (MCP) method, which prunes the momentum encoder instead to obtain a momentum subnet. It maintains an unpruned momentum encoder as a smooth transition scheme to alleviate the representation gap between the encoder and momentum subnet. To fulfill the sparsity requirements of the encoder, alternating direction method of multipliers [40] (ADMM) is adopted. Experiments prove that our MCP method can obtain a momentum subnet that has almost equal performance as the over-parameterized MoCo when transferred to downstream tasks, meanwhile has much less parameters and float operations per second (FLOPs).

关键词： Representation learning Visualization computer vision conferences Computational modeling Supervised learning Self-supervised learning

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LASO: Language-guided Affordance Segmentation on 3D Object

LASO: Language-guided Affordance Segmentation on 3D Object

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

作者： Li, Yicong Zhao, Na Xiao, Junbin Feng, Chun Wang, Xiang Chua, Tat-seng Natl Univ Singapore Singapore Singapore Singapore Univ Technol & Design Singapore Singapore Univ Sci & Technol China Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Inst Dataspace Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

Segmenting affordance in 3D data is key for bridging perception and action in robots. Existing efforts mostly focus on the visual side and overlook the affordance knowledge from a semantic aspect. This oversight not only limits their generalization to unseen objects, but more importantly, hinders their synergy with large language models (LLMs) which are excellent task planners that can decompose an overarching command into agent-actionable instructions. With this regard, we propose a novel task, Language-guided Affordance Segmentation on 3D Object (LASO), which challenges a model to segment a 3D object's part relevant to a given affordance question. To facilitate the task, we contribute a dataset comprising 19,751 point-question pairs, covering 8434 object shapes and 870 expert-crafted questions. As a pioneer solution, we further propose PointRefer, which highlights an adaptive fusion module to identify target affordance regions at different scales. To ensure a text-aware segmentation, we adopt a set of affordance queries conditioned on linguistic cues to generate dynamic kernels. These kernels are further used to convolute with point features and generate a segmentation mask. Comprehensive experiments and analyses validate PointRefer's effectiveness. With these efforts, We hope that LASO can steer the direction of 3D affordance, guiding it towards enhanced integration with the evolving capabilities of LLMs. Code and data are available at https://***/yl3800/LASO.

关键词： 3D-Language Multimodal vision-Language

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SphereGlue: Learning Keypoint Matching on High Resolution Spherical Images

SphereGlue: Learning Keypoint Matching on High Resolution Sp...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Gava, Christiano Mukunda, Vishal Habtegebrial, Tewodros Raue, Federico Palacio, Sebastian Dengel, Andreas Dfki Germany University of Kaiserslautern-Landau Germany

ISBN: (纸本)9798350302493

Traditionally, spherical keypoint matching has been performed using greedy algorithms, such as Nearest Neighbors (NN) search. NN based algorithms often lead to erroneous or insufficient matches as they fail to leverage global keypoint neighborhood information. Inspired by a recent learned perspective matching approach [53] we introduce SphereGlue: a Graph Neural Network based feature matching for high-resolution spherical images. The proposed model naturally handles the severe distortions resulting from geometric transformations. Rigorous evaluations demonstrate the efficacy of SphereGlue in matching both learned and handcrafted keypoints, on synthetic and real high-resolution spherical images. Moreover, SphereGlue generalizes well to previously unseen real-world and synthetic scenes. Results on camera pose estimation show that SphereGlue can directly replace state-of-the-art matching algorithms, in downstream tasks. © 2023 ieee.

关键词： computer vision

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Weakly-Supervised Action Detection Guided by Audio Narration

Weakly-Supervised Action Detection Guided by Audio Narration

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

作者： Ye, Keren Kovashka, Adriana Cruise San Francisco CA 94107 USA Univ Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Videos are more well-organized curated data sources for visual concept learning than images. Unlike the 2-dimensional images which only involve the spatial information, the additional temporal dimension bridges and synchronizes multiple modalities. However, in most video detection benchmarks, these additional modalities are not fully utilized. For example, EPIC Kitchens is the largest dataset in first-person (egocentric) vision, yet it still relies on crowdsourced information to refine the action boundaries to provide instance-level action annotations. We explored how to eliminate the expensive annotations in video detection data which provide refined boundaries. We propose a model to learn from the narration supervision and utilize multimodal features, including RGB, motion flow, and ambient sound. Our model learns to attend to the frames related to the narration label while suppressing the irrelevant frames from being used. Our experiments show that noisy audio narration suffices to learn a good action detection model, thus reducing annotation expenses.

关键词： Visualization Annotations Soft sensors Refining Detectors pattern recognition Synchronization

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Learning Generalized Feature for Temporal Action Detection: Application for Natural Driving Action recognition Challenge

Learning Generalized Feature for Temporal Action Detection: ...

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

作者： Chuong Nguyen Ngoc Nguyen Su Huynh Vinh Nguyen Son Nguyen CyberCore AI Morioka Iwate Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports our approach for the 2022 AI City Challenge - Naturalistic Driving Action recognition (Track 3), where the objective is to detect when and what kinds of actions that a driver performs in a long, untrimmed video. Our solution is built upon the single stage ActionFormer detector, in which temporal location and classification are predicted simultaneously for efficiency. The input feature for the detector is extracted offline using our proposed backbone, which we named "ConvNext-Video". However, due to the small size of the dataset, training the model to avoid over-fitting becomes challenging. To address this problem, we focus on training techniques that can improve the generalization of underlying features. Specifically, we utilize two methods: "learning without forgetting" and semi-weak supervised learning on the unlabeled data A2. Finally, we also add a second-stage classifier (SSC) using our ConvNeXt-Video backbone. The SSC Classifer is designed to combine information from multi-clips and multi-view cameras to improve the prediction precision. Our best result achieves 29.1 F1 score on the public test set. Our source code is released at link.

关键词： Training conferences Urban areas Supervised learning Detectors Feature extraction pattern recognition

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SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics

SpiderNet: Hybrid Differentiable-Evolutionary Architecture S...

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

作者： Geada, Rob McGough, Andrew Stephen Red Hat Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems. However, these algorithms require a variety of design parameters in the form of user configuration or hard-coded decisions which limit the variety of networks that can be discovered. This means that NAS algorithms do not eliminate model design tuning, they instead merely shift the burden of where that tuning needs to be applied. In this paper, we present SpiderNet, a hybrid differentiable-evolutionary and hardware-aware algorithm that rapidly and efficiently produces state-of-the-art networks. More importantly, SpiderNet is a proof-of-concept of a minimally-configured NAS algorithm;the majority of design choices seen in other algorithms are incorporated into SpiderNet's dynamically-evolving search space, minimizing the number of user choices to just two: reduction cell count and initial channel count. SpiderNet produces models highly-competitive with the state-of-the-art, and outperforms random search in accuracy, runtime, memory size, and parameter count.

关键词： Measurement Runtime Heuristic algorithms Microprocessors Neural networks computer architecture Manuals

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A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings

A Modular Multimodal Architecture for Gaze Target Prediction...

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

作者： Gupta, Anshul Tafasca, Samy Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case. (1)

关键词： Three-dimensional displays Fuses Surveillance computer architecture Predictive models Skeleton pattern recognition

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Online Unsupervised Domain Adaptation for Person Re-identification

Online Unsupervised Domain Adaptation for Person Re-identifi...

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

作者： Rami, Hamza Ospici, Matthieu Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Paris France Atos London England

ISBN: (纸本)9781665487399

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical realworld application, where data is continuously increasing from the different network's cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.

关键词： Training Adaptation models Privacy computer vision conferences Benchmark testing Data models

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Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological Images

Tumor Micro-environment Interactions Guided Graph Learning f...

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

作者： Shao, Wei Shi, YangYang Zhang, Daoqiang Zhou, JunJie Wan, Peng Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Key Lab Brain Machine Intelligence Technol Minist Educ Nanjing Peoples R China MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However, most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs, which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact, tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME), and the de-tailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper, we considered the spatial interactions among tumor and its two major TME components (i.e., lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically, we firstly selected different types of patches as nodes to build graph for each WSI. Then, a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally, a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models, but also has good explainable ability for survival prediction.

关键词： Survival Analysis

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