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检索条件"主题词=Image recognition and understanding"

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Contextual Affinity Distillation for image Anomaly Detection

Contextual Affinity Distillation for Image Anomaly Detection

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Zhang, Jie Suganuma, Masanori Okatani, Takayuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan RIKEN Ctr AIP Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Previous studies on unsupervised industrial anomaly detection mainly focus on 'structural' types of anomalies such as cracks and color contamination by matching or learning local feature representations. While achieving significantly high detection performance on this kind of anomaly, they are faced with 'logical' types of anomalies that violate the long-range dependencies such as a normal object placed in the wrong position. Noting the reverse distillation approaches that are under the encoder-decoder paradigm could learn from the high abstract level knowledge, we propose to use two students (local and global) to better mimic the teacher's local and global behavior in reverse distillation. The local student, which is used in previous studies mainly focuses on accurate local feature learning while the global student pays attention to learning global correlations. To further encourage the global student's learning to capture long-range dependencies, we design the global context condensing block (GCCB) and propose a contextual affinity loss for the student training and anomaly scoring. Experimental results show that the proposed method sets a new state-of-the-art performance on the MVTec LOCO AD dataset without using complex training techniques.

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

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Prototypical Contrastive Network for Imbalanced Aerial image Segmentation

Prototypical Contrastive Network for Imbalanced Aerial Image...

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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Nogueira, Keiller Faita-Pinheiro, Mayara Maezano Marques Ramos, Ana Paula Goncalves, Wesley Nunes Marcato Junior, Jose, Jr. dos Santos, Jefersson A. Univ Stirling Stirling FK9 4LA Scotland Univ Western Sao Paulo UNOESTE Presidente Prudente SP Brazil Sao Paulo State Univ UNESP Presidente Prudente SP Brazil Fed Univ Mato Grosso do Sul UFMS Campo Grande MS Brazil Univ Sheffield Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9798350318920;9798350318937

Binary segmentation is the main task underpinning several remote sensing applications, which are particularly interested in identifying and monitoring a specific category/object. Although extremely important, such a task has several challenges, including huge intra-class variance for the background and data imbalance. Furthermore, most works tackling this task partially or completely ignore one or both of these challenges and their developments. In this paper, we propose a novel method to perform imbalanced binary segmentation of remote sensing images based on deep networks, prototypes, and contrastive loss. The proposed approach allows the model to focus on learning the foreground class while alleviating the class imbalance problem by allowing it to concentrate on the most difficult background examples. The results demonstrate that the proposed method outperforms state-of-the-art techniques for imbalanced binary segmentation of remote sensing images while taking much less training time.

关键词： Algorithms Algorithms and algorithms Applications formulations image recognition and understanding Machine learning architectures Remote Sensing

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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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Disentangled Pre-training for image Matting

Disentangled Pre-training for Image Matting

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Li, Yanda Huang, Zilong Yu, Gang Chen, Ling Wei, Yunchao Jiao, Jianbo Univ Technol Sydney Sydney NSW Australia Tencent Shenzhen Peoples R China Beijing Jiaotong Univ Beijing Peoples R China Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9798350318920;9798350318937

image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by proposing a self-supervised pre-training approach that can leverage infinite numbers of data to boost the matting performance. The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective. The pre-trained model is then used as an initialisation of the downstream matting task for fine-tuning. Extensive experimental evaluations show that the proposed approach outperforms both the state-of-the-art matting methods and other alternative self-supervised initialisation approaches by a large margin. We also show the robustness of the proposed approach over different backbone architectures. Our project page is available at https://***/dpt_mat/.

关键词： Algorithms Algorithms image recognition and understanding Low-level and physics-based vision

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C²AIR: Consolidated Compact Aerial image Haze Removal

C<SUP>2</SUP>AIR: Consolidated Compact Aerial Image Haze Rem...

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Kulkarni, Ashutosh Phutke, Shruti S. Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Inst Technol Ropar CVPR Lab Ropar India Griffith Univ Inst Integrated & Intelligent Syst Griffith NSW Australia Trinity Coll Dublin CVPR Lab Sch Comp Sci & Stat Dublin Ireland

ISBN: (纸本)9798350318920;9798350318937

Aerial image haze removal deals with improving the visibility and quality of images captured from aerial platforms, such as drones and satellites. Aerial images are commonly used in various applications such as environmental monitoring, and disaster response. These applications usually require cleaner data for accurate functioning. However, atmospheric conditions such as haze or fog can significantly degrade the quality of these images, reducing their contrast, color saturation, and sharpness, making it difficult to extract meaningful information from them. Existing methods rely on computationally heavy and haze density (light, moderate, dense) specific architectures for aerial image dehazing. In light of these limitations, we propose a novel lightweight and consolidated approach for aerial image dehazing. In this approach, we propose Density Aware Query Modulated Block for learning weather degradations in input features and guiding the restoration process. Further, we propose Cross Collaborative Feed-Forward Block for learning to restore varying sizes of the structures in the input images. Finally, we propose Gated Adaptive Feature Fusion block to achieve inter-scale and intra-feature attentive fusion, effective for aerial image restoration. Extensive analysis on benchmark aerial image dehazing datasets and real-world images, along with detailed ablation studies validate the effectiveness of the proposed approach. Further, we have analysed our method for other restoration task such as underwater image enhancement to experiment its wide applicability. The code is available at https://***/AshutoshKulkarni4998/C2AIR.

关键词： Algorithms Algorithms image recognition and understanding Video recognition and understanding

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Self-Sampling Meta SAM: Enhancing Few-shot Medical image Segmentation with Meta-Learning

Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Seg...

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Leng, Tianang Zhang, Yiming Han, Kun Xie, Xiaohui Huazhong Univ Sci & Technol Wuhan Peoples R China Tokyo Inst Technol Tokyo Japan Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9798350318920;9798350318937

While the Segment Anything Model (SAM) excels in semantic segmentation for general-purpose images, its performance significantly deteriorates when applied to medical images, primarily attributable to insufficient representation of medical images in its training dataset. Nonetheless, gathering comprehensive datasets and training models that are universally applicable is particularly challenging due to the long-tail problem common in medical images. To address this gap, here we present a Self-Sampling Meta SAM (SSM-SAM) framework for few-shot medical image segmentation. Our innovation lies in the design of three key modules: 1) An online fast gradient descent optimizer, further optimized by a meta-learner, which ensures swift and robust adaptation to new tasks. 2) A Self-Sampling module designed to provide well-aligned visual prompts for improved attention allocation;and 3) A robust attention-based decoder specifically designed for few-shot medical image segmentation to capture relationship between different slices. Extensive experiments on a popular abdominal CT dataset and an MRI dataset demonstrate that the proposed method achieves significant improvements over state-of-the-art methods in few-shot segmentation, with an average improvements of 10.21% and 1.80% in terms of DSC, respectively. In conclusion, we present a novel approach for rapid online adaptation in interactive image segmentation, adapting to a new organ in just 0.83 minutes. Code is available at https://***/DragonDescentZerotsu/SSM-SAM

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations image recognition and understanding Machine learning architectures

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Handformer2T: A Lightweight Regression-based Model for Interacting Hands Pose Estimation from A Single RGB image

Handformer2T: A Lightweight Regression-based Model for Inter...

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Zhang, Pengfei Kong, Deying Univ Calif Irvine Irvine CA 92617 USA Google Mountain View CA USA

ISBN: (纸本)9798350318920;9798350318937

Despite its extensive range of potential applications in virtual reality and augmented reality, 3D interacting hand pose estimation from RGB image remains a very challenging problem, due to appearance confusions between keypoints of the two hands, and severe hand-hand occlusion. Due to their ability to capture long range relationships between keypoints, transformer-based methods have gained popularity in the research community. However, the existing methods usually deploy tokens at keypoint level, which inevitably results in high computational and memory complexity. In this paper, we propose a simple yet novel mechanism, i.e., hand-level tokenization, in our transformer based model, where we deploy only one token for each hand. With this novel design, we also propose a pose query enhancer module, which can refine the pose prediction iteratively, by focusing on features guided by previous coarse pose predictions. As a result, our proposed model, Handformer2T, can achieve high performance while maintaining lightweight. Extensive experiments on public benchmarks demonstrate that our model can achieve state-of-the-art performance on interacting-hand pose estimation with higher throughput, less memory and faster speed.

关键词： 3D computer vision Algorithms Algorithms Algorithms Biometrics body pose face gesture image recognition and understanding

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Conditional Velocity Score Estimation for image Restoration

Conditional Velocity Score Estimation for Image Restoration

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Shi, Ziqiang Liu, Rujie Fujitsu R&D Ctr Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

This paper proposes a new image restoration method by introducing a velocity variable on top of the data position during recovery. Under the guidance of the degraded image, it can effectively and dynamically control the direction of the diffusion path in the reverse-time stochastic differential equation (SDE). So the crucial factor is how to combine the degraded signal as a guide in this second-order reverse process with velocity, especially in the moving direction as a diffusion path. To this end, we propose a conditional velocity score approximation (CVSA) method based on the Bayesian principle to approximate the true posterior conditional velocity score by the sum of a priori conditional velocity score and an observation velocity score of the degraded measurement at the current moment. Our method is versatile from two perspectives. It can be used for both non-blind restoration and blind restoration. At the same time, there is almost no requirement for the degradation operator, and both linear and nonlinear tasks are acceptable. In non-blind restoration, including deblurring, inpainting, super-resolution, phase retrieval, and blind restoration, such as deblurring experiments, CVSA is better than other methods and achieves a new state-of-the-art.

关键词： 3D Algorithms Algorithms Algorithms etc. Generative models for image image recognition and understanding Low-level and physics-based vision video

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SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical image Segmentation

SynergyNet: Bridging the Gap between Discrete and Continuous...

引用

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Gorade, Vandan Mittal, Sparsh Jha, Debesh Bagci, Ulas Indian Inst Technol Roorkee Uttar Pradesh India Northwestern Univ Chicago IL USA

ISBN: (纸本)9798350318920;9798350318937

In recent years, continuous latent space (CLS) and discrete latent space (DLS) deep learning models have been proposed for medical image analysis for improved performance. However, these models encounter distinct challenges. CLS models capture intricate details but often lack interpretability in terms of structural representation and robustness due to their emphasis on low-level features. Conversely, DLS models offer interpretability, robustness, and the ability to capture coarse-grained information thanks to their structured latent space. However, DLS models have limited efficacy in capturing fine-grained details. To address the limitations of both DLS and CLS models, we propose SynergyNet, a novel bottleneck architecture designed to enhance existing encoder-decoder segmentation frameworks. SynergyNet seamlessly integrates discrete and continuous representations to harness complementary information and successfully preserves both fine and coarsegrained details in the learned representations. Our extensive experiment on multi-organ segmentation and cardiac datasets demonstrates that SynergyNet outperforms other state of the art methods including TransUNet: dice scores improving by 2.16%, and Hausdorff scores improving by 11.13%, respectively. When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1.71% in Intersection-over-Union scores for skin lesion segmentation and of 8.58% for brain tumor segmentation. Our innovative approach paves the way for enhancing the overall performance and capabilities of deep learning models in the critical domain of medical image analysis.

关键词： Algorithms Applications Biomedical / healthcare / medicine image recognition and understanding

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Beyond Self-Attention: Deformable Large Kernel Attention for Medical image Segmentation

Beyond Self-Attention: Deformable Large Kernel Attention for...

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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Azad, Reza Niggemeier, Leon Huettemann, Michael Kazerouni, Amirhossein Aghdam, Ehsan Khodapanah Velichko, Yury Bagci, Ulas Merhof, Dorit Rhein Westfal TH Aachen Aachen Germany Iran Univ Sci & Technol Tehran Iran Shahid Beheshti Univ Tajrish Iran Northwestern Univ Evanston IL USA Univ Regensburg Regensburg Germany

ISBN: (纸本)9798350318920;9798350318937

Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the increasing computational demands of these models, proportional to the squared token count, limit their depth and resolution capabilities. Most current methods process D volumetric image data slice-by-slice (called pseudo 3D), missing crucial inter-slice information and thus reducing the model's overall performance. To address these challenges, we introduce the concept of Deformable Large Kernel Attention (D-LKA Attention), a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context. This mechanism operates within a receptive field akin to self-attention while sidestepping the computational overhead. Additionally, our proposed attention mechanism benefits from deformable convolutions to flexibly warp the sampling grid, enabling the model to adapt appropriately to diverse data patterns. We designed both 2D and 3D adaptations of the D-LKA Attention, with the latter excelling in cross-depth data understanding. Together, these components shape our novel hierarchical Vision Transformer architecture, the D-LKA Net. Evaluations of our model against leading methods on popular medical segmentation datasets (Synapse, NIH Pancreas, and Skin lesion) demonstrate its superior performance. Our code is publicly available at GitHub.

关键词： Algorithms Applications Biomedical / healthcare / medicine image recognition and understanding

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