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CXR-IRGen: An Integrated vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs

CXR-IRGen: An Integrated Vision and Language Model for the G...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Shentu, Junjie Al Moubayed, Noura Univ Durham Durham England

ISBN: (纸本)9798350318920;9798350318937

Chest X-Ray (CXR) images play a crucial role in clinical practice, providing vital support for diagnosis and treatment. Augmenting the CXR dataset with synthetically generated CXR images annotated with radiology reports can enhance the performance of deep learning models for various tasks. However, existing studies have primarily focused on generating unimodal data of either images or reports. In this study, we propose an integrated model, CXR-IRGen, designed specifically for generating CXR image-report pairs. Our model follows a modularized structure consisting of a vision module and a language module. Notably, we present a novel prompt design for the vision module by combining both text embedding and image embedding of a reference image. Additionally, we propose a new CXR report generation model as the language module, which effectively leverages a large language model and self-supervised learning strategy. Experimental results demonstrate that our new prompt is capable of improving the general quality (FID) and clinical efficacy (AUROC) of the generated images, with average improvements of 15.84% and 1.84%, respectively. Moreover, the proposed CXR report generation model outperforms baseline models in terms of clinical efficacy (F-1 score) and exhibits a high-level alignment of image and text, as the best F-1 score of our model is 6.93% higher than the state-of-the-art CXR report generation model. Our code is available at https://***/junjie-shentu/CXR-IRGen.

关键词： 3D Algorithms Algorithms etc. Generative models for image Image recognition and understanding video

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Total Variation Optimization Layers for computer vision

Total Variation Optimization Layers for Computer Vision

引用

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

作者： Yeh, Raymond A. Hu, Yuan-Ting Ren, Zhongzheng Schwing, Alexander G. Toyota Technol Inst Chicago IL 60637 USA Univ Illinois Champaign IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?;(b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projectedNewton method which is 37x faster than existing solutions.

关键词： Location awareness computer vision TV Smoothing methods Image edge detection Minimization pattern recognition

来源：评论

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Revisiting Near/Remote Sensing with Geospatial Attention

Revisiting Near/Remote Sensing with Geospatial Attention

引用

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

作者： Workman, Scott Rafique, M. Usman Blanton, Hunter Jacobs, Nathan DZYNE Technol Fairfax VA 22031 USA Kitware Inc Clifton Pk NY USA Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.

关键词： Image segmentation computer vision Social networking (online) Computational modeling computer architecture Geospatial analysis Sensors

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SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data

SCoRD: Subject-Conditional Relation Detection with Text-Augm...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yang, Ziyan Kafle, Kushal Lin, Zhe Cohen, Scott Ding, Zhihong Ordonez, Vicente Rice Univ Houston TX 77251 USA Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350318920;9798350318937

We propose Subject-Conditional Relation Detection (SCoRD), where conditioned on an input subject, the goal is to predict all its relations to other objects in a scene along with their locations. Based on the Open Images dataset, we propose a challenging OIv6-SCoRD benchmark such that the training and testing splits have a distribution shift in terms of the occurrence statistics of < subject, relation, object > triplets. To solve this problem, we propose an auto-regressive model that given a subject, it predicts its relations, objects, and object locations by casting this output as a sequence of tokens. First, we show that previous scene-graph prediction methods fail to produce as exhaustive an enumeration of relation-object pairs when conditioned on a subject on this benchmark. Particularly, we obtain a recall@3 of 83.8% for our relation-object predictions compared to the 49.75% obtained by a recent scene graph detector. Then, we show improved generalization on both relation-object and object-box predictions by leveraging during training relation-object pairs obtained automatically from textual captions and for which no object-box annotations are available. Particularly, for < subject, relation, object > triplets for which no object locations are available during training, we are able to obtain a recall@3 of 33.80% for relation-object pairs and 26.75% for their box locations.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segme...

引用

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

作者： Yu, Qihang Wang, Huiyu Kim, Dahun Qiao, Siyuan Collins, Maxwell Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Korea Adv Inst Sci & Technol Daejeon South Korea Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering. It rethinks the existing transformer architectures used in segmentation and detection;CMT-DeepLab considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation. The clustering is computed with an alternating procedure, by first assigning pixels to the clusters by their feature affinity, and then updating the cluster centers and pixel features. Together, these operations comprise the Clustering Mask Transformer (CMT) layer, which produces cross-attention that is denser and more consistent with the final segmentation task. CMT-DeepLab improves the performance over prior art significantly by 4.4% PQ, achieving a new state-of-the-art of 55.7% PQ on the COCO test-dev set.

关键词： computer vision Art computer architecture Transformers pattern recognition Task analysis

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Revisiting Token Pruning for Object Detection and Instance Segmentation

Revisiting Token Pruning for Object Detection and Instance S...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Liu, Yifei Gehrig, Mathias Messikommer, Nico Cannici, Marco Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350318920;9798350318937

vision Transformers (ViTs) have shown impressive performance in computer vision, but their high computational cost, quadratic in the number of tokens, limits their adoption in computation-constrained applications. However, this large number of tokens may not be necessary, as not all tokens are equally important. In this paper, we investigate token pruning to accelerate inference for object detection and instance segmentation, extending prior works from image classification. Through extensive experiments, we offer four insights for dense tasks: (i) tokens should not be completely pruned and discarded, but rather preserved in the feature maps for later use. (ii) reactivating previously pruned tokens can further enhance model performance. (iii) a dynamic pruning rate based on images is better than a fixed pruning rate. (iv) a lightweight, 2-layer MLP can effectively prune tokens, achieving accuracy comparable with complex gating networks with a simpler design. We assess the effects of these design decisions on the COCO dataset and introduce an approach that incorporates these findings, showing a reduction in performance decline from similar to 1.5 mAP to similar to 0.3 mAP in both boxes and masks, compared to existing token pruning methods. In relation to the dense counterpart that utilizes all tokens, our method realizes an increase in inference speed, achieving up to 34% faster performance for the entire network and 46% for the backbone. Code: https://***/uzh-rpg/svit/

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Cross-domain Few-shot Learning with Task-specific Adapters

Cross-domain Few-shot Learning with Task-specific Adapters

引用

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

作者： Li, Wei-Hong Liu, Xialei Bilen, Hakan Univ Edinburgh VICO Grp Edinburgh Midlothian Scotland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. Recent approaches broadly solve this problem by parameterizing their few-shot classifiers with task-agnostic and task-specific weights where the former is typically learned on a large training set and the latter is dynamically predicted through an auxiliary network conditioned on a small support set. In this work, we focus on the estimation of the latter, and propose to learn task-specific weights from scratch directly on a small support set, in contrast to dynamically estimating them. In particular, through systematic analysis, we show that task-specific weights through parametric adapters in matrix form with residual connections to multiple intermediate layers of a backbone network significantly improves the performance of the state-of-the-art models in the Meta-Dataset benchmark with minor additional cost.

关键词： Training computer vision Analytical models Systematics Costs Computational modeling Estimation

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Incremental Learning in Semantic Segmentation from Image Labels

Incremental Learning in Semantic Segmentation from Image Lab...

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

作者： Cermelli, Fabio Fontanel, Dario Tavera, Antonio Ciccone, Marco Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This paper proposes a novel framework for Weakly Incremental Learning for Semantic Segmentation, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudolabels offline, we use a localizes, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally. We cope with the inherent noise in the process by using soft-labels generated by the localizes. We demonstrate the effectiveness of our approach on the Pascal VOC and COCO datasets, outperforming offline weakly-supervised methods and obtaining results comparable with incremental learning methods with full supervision.(1)

关键词： Learning systems Location awareness Image segmentation computer vision Protocols Annotations Semantics

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Plenoxels: Radiance Fields without Neural Networks

Plenoxels: Radiance Fields without Neural Networks

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

作者： Fridovich-Keil, Sara Yu, Alex Tancik, Matthew Chen, Qinhong Recht, Benjamin Kanazawa, Angjoo Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://***/plenoxels.

关键词： Visualization Gradient methods computer vision Three-dimensional displays Codes Neural networks Benchmark testing

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Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

Limited Data, Unlimited Potential: A Study on ViTs Augmented...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Das, Srijan Jain, Tanmay Reilly, Dominick Balaji, Pranav Karmakar, Soumyajit Marjit, Shyam Li, Xiang Das, Abhijit Ryoo, Michael S. UNC Charlotte Charlotte NC 28223 USA Delhi Technol Univ Delhi India BITS Pilani Hyderabad Secunderabad India Indian Inst Informat Technol Guwahati Gauhati Assam India SUNY Stony Brook Stony Brook NY USA

ISBN: (纸本)9798350318920;9798350318937

vision Transformers (ViTs) have become ubiquitous in computer vision. Despite their success, ViTs lack inductive biases, which can make it difficult to train them with limited data. To address this challenge, prior studies suggest training ViTs with self-supervised learning (SSL) and fine-tuning sequentially. However, we observe that jointly optimizing ViTs for the primary task and a Self-Supervised Auxiliary Task (SSAT) is surprisingly beneficial when the amount of training data is limited. We explore the appropriate SSL tasks that can be optimized alongside the primary task, the training schemes for these tasks, and the data scale at which they can be most effective. Our findings reveal that SSAT is a powerful technique that enables ViTs to leverage the unique characteristics of both the self-supervised and primary tasks, achieving better performance than typical ViTs pre-training with SSL and fine-tuning sequentially. Our experiments, conducted on 10 datasets, demonstrate that SSAT significantly improves ViT performance while reducing carbon footprint. We also confirm the effectiveness of SSAT in the video domain for deepfake detection, showcasing its generalizability. Our code is available at https:// ***/dominickrei/Limiteddata- vits.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures Video recognition and understanding

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