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The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector

The Treasure Beneath Multiple Annotations: An Uncertainty-aw...

引用

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

作者： Zhou, Caixia Huang, Yaping Pu, Mengyang Guan, Qingji Huang, Li Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY USA

ISBN: (纸本)9798350301298

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://***/ZhouCX117/UAED.

关键词： Low-level vision

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Adaptive Channel Sparsity for Federated Learning under System Heterogeneity

Adaptive Channel Sparsity for Federated Learning under Syste...

引用

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

作者： Liao, Dongping Gao, Xitong Zhao, Yiren Xu, Chengzhong Univ Macau State Key Lab IoTSC CIS Dept Taipa Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Imperial Coll London London England

ISBN: (纸本)9798350301298

Owing to the non-i.i.d. nature of client data, channel neurons in federated-learned models may specialize to distinct features for different clients. Yet, existing channel-sparse federated learning (FL) algorithms prescribe fixed sparsity strategies for client models, and may thus prevent clients from training channel neurons collaboratively. To minimize the impact of sparsity on FL convergence, we propose Flado to improve the alignment of client model update trajectories by tailoring the sparsities of individual neurons in each client. Empirical results show that while other sparse methods are surprisingly impactful to convergence, Flado can not only attain the highest task accuracies with unlimited budget across a range of datasets, but also significantly reduce the amount of floating-point operations (FLOPs) required for training more than by 10 x under the same communications budget, and push the Pareto frontier of communication/computation trade-off notably further than competing FL algorithms.

关键词： Efficient and scalable vision

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ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

ShadowDiffusion: When Degradation Prior Meets Diffusion Mode...

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

作者： Guo, Lanqing Wang, Chong Yang, Wenhan Huang, Siyu Wang, Yufei Pfister, Hanspeter Wen, Bihan Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9798350301298

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.(1)

关键词： Low-level vision

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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

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

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

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Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

Mask DINO: Towards A Unified Transformer-based Framework for...

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

作者： Li, Feng Zhang, Hao Xu, Huaizhe Liu, Shilong Zhang, Lei Ni, Lionel M. Shum, Heimg-Yeung Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Inst AI Dept CST BNIZist Ctr Beijing Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798350301298

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at https://github. com/ IDEAResearch/MaskDINO.

关键词： vision applications and systems

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Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

Optimization-Inspired Cross-Attention Transformer for Compre...

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

作者： Song, Jiechong Mou, Chong Wang, Shiqi Ma, Siwei Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://***/songjiechong/OCTUF.

关键词： Low-level vision

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Label-Free Liver Tumor Segmentation

Label-Free Liver Tumor Segmentation

引用

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

作者： Hu, Qixin Chen, Yixiong Xiao, Junfei Sun, Shuwen Chen, Jieneng Yuille, Alan Zhou, Zongwei Huazhong Univ Sci & Technol Wuhan Peoples R China Chinese Univ Hong Kong Shenzhen Shenzhen Peoples R China Johns Hopkins Univ Baltimore MD USA Nanjing Med Univ Affiliated Hosp 1 Nanjing Peoples R China

ISBN: (纸本)9798350301298

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors;(II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors-this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.

关键词： cell microscopy Medical and biological vision

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Uncovering the Hidden Cost of Model Compression

Uncovering the Hidden Cost of Model Compression

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

作者： Misra, Diganta Chaudhary, Muawiz Goyal, Agam Runwal, Bharat Chen, Pin Yu Carnegie Mellon Univ Pittsburgh PA 15213 USA Landskape AI Leiden Netherlands Mila Quebec AI Inst Montreal PQ Canada Concordia Univ Montreal PQ Canada Univ Wisconsin Madison Madison WI USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798350365474

In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer vision. As the importance of efficiency continues to rise, research into model compression has become indispensable in alleviating the computational burdens associated with training and deploying over-parameterized neural networks. A primary objective in model compression is to develop sparse and/or quantized models capable of matching or even surpassing the performance of their over-parameterized, full-precision counterparts. Although previous studies have explored the effects of model compression on transfer learning, its impact on visual prompting-based transfer remains unclear. This study aims to bridge this gap, shedding light on the fact that model compression detrimentally impacts the performance of visual prompting-based transfer, particularly evident in scenarios with low data volume. Furthermore, our findings underscore the adverse influence of sparsity on the calibration of downstream visual-prompted models. However, intriguingly, we also illustrate that such negative effects on calibration are not present when models are compressed via quantization. This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.

关键词： Compression Quantization Sparsity vision prompting

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

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

作者： Perera, Shehan Navard, Pouyan Yilmaz, Alper Ohio State Univ Photogrammetr Comp Vis Lab Columbus OH 43210 USA

ISBN: (纸本)9798350365474

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33x less parameters and a 13x reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： 3D Medical Image Segmentation ACDC Attention BraTs Deep Learning Efficient Attention Segmentation Synapse Transformers vision Transformers

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CRAFT: Concept Recursive Activation FacTorization for Explainability

CRAFT: Concept Recursive Activation FacTorization for Explai...

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

作者： Fel, Thomas Picard, Agustin Bethune, Louis Boissin, Thibaut Vigouroux, David Colin, Julien Cadene, Remi Serre, Thomas Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Sorbonne Univ CNRS Paris France Univ Toulouse Artificial & Nat Intelligence Toulouse Inst Toulouse France Inst Rech Technol St Exupery Toulouse France SNCF Innovat & Res Div St Denis France Scalian Rennes France

ISBN: (纸本)9798350301298

Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image - revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas. In this work, we try to fill in this gap with CRAFT - a novel approach to identify both "what" and "where" by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (i) a recursive strategy to detect and decompose concepts across layers, (ii) a novel method for a more faithful estimation of concept importance using Sobol indices, and (iii) the use of implicit differentiation to unlock Concept Attribution Maps. We conduct both human and computer vision experiments to demonstrate the benefits of the proposed approach. We show that the proposed concept importance estimation technique is more faithful to the model than previous methods. When evaluating the usefulness of the method for human experimenters on a human-centered utility benchmark, we find that our approach significantly improves on two of the three test scenarios.

关键词： Explainable computer vision

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