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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

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

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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Multi-View Spatial-Temporal Learning for Understanding Unusual Behaviors in Untrimmed Naturalistic Driving Videos

Multi-View Spatial-Temporal Learning for Understanding Unusu...

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

作者： Huy-Hung Nguyen Chi Dai Tran Long Hoang Pham Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Duong Khac Vu Quoc Pham-Nam Ho Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The task of Naturalistic Driving Action recognition aims to detect and temporally localize distracting driving behavior in untrimmed videos. In this paper, we introduce our framework for Track 3 of the 8 th AI City Challenge in 2024. The approach is primarily based on large model fine-tuning and ensemble techniques to train a set of action recognition models on a small-scale dataset. Starting with raw videos, we segment them into individual action sequences based on their annotation. We then fine-tune four different action recognition models, with K-fold cross-validation applied to the segmented data. Following this, we execute a multi-view ensemble, selecting the most visible camera views for each action class to generate clip-level classification results for each video. Finally, a multi-step post-processing algorithm, which is designed for the AI City Challenge dataset’s specific features, is employed to perform temporal action localization and produce temporal segments for the actions. Our solution achieves a final mOS score of 0.7798 and attains the 5 th rank on the public leaderboard for the test set A2 of the challenge. The source code will be publicly available at https://***/SKKUAutoLab/AIC24-Track03.

关键词： Location awareness Source coding Urban areas X3D Prediction algorithms Classification algorithms pattern recognition

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Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients

Cross-Domain Knowledge Transfer for Prediction of Chemosensi...

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

作者： Asfand Yaar Amina Asif Shan E Ahmed Raza Nasir Rajpoot Fayyaz Minhas Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Tissue Image Analytics Lab & PathLAKE CoE University of Warwick UK

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we report a novel deep neural network framework for prediction of chemo-sensitivity in ovarian cancer patients. The proposed model is based on Multiple Instance Learning (MIL) and a novel variant of Learning using Privileged Information (LUPI). LUPI allows knowledge transfer from highly informative privileged features that are available only at training time to give improved generalization performance on input space features which are available in both training and inference. The proposed model is trained on image patches from Hematoxylin and Eosin (H&E) stained multi-gigapixel whole-slide images (WSIs, the input space) of ovarian cancer tissue sections and their associated gene expression profiles, the privileged feature space. Through cross-domain knowledge transfer with a novel combination of MIL and LUPI, we achieve improved generalization with a limited number of labeled examples in the input space. Informed by the privileged space model output based on relatively expensive and time-consuming gene expression profiles in its training, the proposed LUPI model can generate accurate predictions using routine WSI data alone at the time of inference. The proposed method paves the way for further applications of LUPI in computational pathology and medical image analysis by cross-domain learning especially in cases with a limited number of labeled examples in training.

关键词： Training Predictive models Gene expression Cancer Machine learning Data models Chemotherapy

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Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling

Repeat and Concatenate: 2D to 3D Image Translation with 3D t...

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

作者： Abril Corona-Figueroa Hubert P. H. Shum Chris G. Willcocks Department of Computer Science Durham University Durham UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.

关键词： Training Solid modeling Three-dimensional displays Uncertainty Encoding pattern recognition Iterative methods

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End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates

End-to-End Neural Network Compression via l1/l2 Regularized ...

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

作者： Anshul Nasery Hardik Shah Arun Sai Suggala Prateek Jain University of Washington ETH Zurich Google Research India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model’s Floating Point Operations (FLOPs) via a novel $\frac{{{\ell _1}}}{{{\ell _2}}}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization, and can optimize for on-device latency. Crucially, it is fast and runs in almost the same amount of time as a single model training run; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve 50% reduction in FLOPs with only 1% drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve 15% reduction in FLOPs without drop in accuracy, while still requiring 3× less training compute than SOTA NAS techniques. Finally, for transfer learning on smaller datasets, our technique identifies 1.2× - 1.4× cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.

关键词： Training Quantization (signal) Image coding Accuracy Costs Transfer learning computer architecture

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Minimizing Supervision in Multi-label Categorization

Minimizing Supervision in Multi-label Categorization

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

作者： Rajat Munender Varshney Pravendra Singh Vinay P. Namboodiri Department of Computer Science and Engineering IIT Kanpur India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.

关键词： Measurement Task analysis Machine learning Welding Feature extraction Uncertainty Computational modeling

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Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks

Gaussian Splatting Decoder for 3D-aware Generative Adversari...

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

作者： Florian Barthel Arian Beckmann Wieland Morgenstern Anna Hilsmann Peter Eisert Fraunhofer Heinrich Hertz Institute HHI Humboldt University of Berlin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providing an explicit 3D representation that can be rendered efficiently at high frame rates. In this work, we present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally, our approach allows for a high resolution GAN inversion and real-time GAN editing with 3D Gaussian Splatting *** page: ***/gaussian_decoder

关键词： Training Visualization Video games Three-dimensional displays Image resolution Generative adversarial networks Rendering (computer graphics)

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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

AIS 2024 Challenge on Video Quality Assessment of User-Gener...

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

作者： Marcos V. Conde Saman Zadtootaghaj Nabajeet Barman Radu Timofte Chenlong He Qi Zheng Ruoxi Zhu Zhengzhong Tu Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Zicheng Zhang Haoning Wu Yingjie Zhou Chunyi Li Xiaohong Liu Weisi Lin Guangtao Zhai Wei Sun Yuqin Cao Yanwei Jiang Jun Jia Zhichao Zhang Zijian Chen Weixia Zhang Xiongkuo Min Steve Göring Zihao Qi Chen Feng CAIDAS & IFI Computer Vision Lab. University of Würzburg Sony Interactive Entertainment FTG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

关键词： Surveys Learning systems Video on demand Reviews User-generated content Quality assessment pattern recognition

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SUNDIAL: 3D Satellite Understanding through Direct, Ambient, and Complex Lighting Decomposition

SUNDIAL: 3D Satellite Understanding through Direct, Ambient,...

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

作者： Nikhil Behari Akshat Dave Kushagra Tiwary William Yang Ramesh Raskar Harvard University Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Estimating 3D geometry of terrain from satellite imagery is essential for environmental science, urban planning, agriculture, and disaster response. However, traditional 3D modeling techniques face unique challenges in the remote sensing context, including limited multi-view baselines over extensive regions, varying direct, ambient, and complex illumination conditions, and time-varying scene changes across captures. In this work, we introduce SUNDIAL, a comprehensive approach to 3D reconstruction of satellite imagery using neural radiance fields. We jointly learn satellite scene geometry, illumination components, and sun direction in this single-model approach, and propose a secondary shadow ray casting technique to 1) improve scene geometry using oblique sun angles to render shadows, 2) enable physically-based disentanglement of scene albedo and illumination, and 3) determine the components of illumination from direct, ambient (sky), and complex sources. To achieve this, we incorporate lighting cues and geometric priors from remote sensing literature in a neural rendering approach, modeling physical properties of satellite scenes such as shadows, scattered sky illumination, and complex illumination of vegetation and water. We evaluate the performance of SUNDIAL against existing NeRF-based techniques for satellite scene modeling and demonstrate improved scene and lighting disentanglement, novel view and lighting rendering, and geometry and sun direction estimation on challenging scenes with small baselines, sparse inputs, and variable illumination.

关键词： Geometry Casting Satellites Three-dimensional displays Lighting Rendering (computer graphics) Satellite images

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