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SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptat...

引用

ieee workshop on applications of computer vision (WACV)

作者： Javier Gamazo Tejero Moritz Schmid Pablo Márquez Neila Martin S. Zinkernagel Sebastian Wolf Raphael Sznitman University of Bern Inselspital Bern Switzerland

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models like SAM on natural images, they struggle with medical domain images. beyond this, recent approaches that perform end-to-end fine-tuning of models are simply not computationally tractable. To address this, we propose a novel SAM adapter approach that minimizes the number of trainable parameters while achieving comparable performances to full fine-tuning. The proposed SAM adapter is strategically placed in the mask decoder, offering excellent and broad generalization capabilities and improved segmentation across both fully supervised and test-time domain adaptation tasks. Extensive validation on four datasets showcases the adapter's efficacy, outperforming existing methods while training less than 1% of SAM's total parameters.

关键词： Training Adaptation models computer vision Computational modeling Semantic segmentation Magnetic resonance imaging Decoding Biomedical imaging

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Breaking the Frame: Visual Place Recognition by Overlap Prediction

Breaking the Frame: Visual Place Recognition by Overlap Pred...

引用

ieee workshop on applications of computer vision (WACV)

作者： Tong Wei Philipp Lindenberger Jiří Matas Daniel Barath Visual Recognition Group FEE Czech Technical University in Prague Computer Vision and Geometry Group ETH Zurich HUN-REN SZTAKI

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual place recognition methods struggle with occlusion and partial visual overlaps. We propose a novel visual place recognition approach based on overlap prediction, called VOP, shifting from traditional reliance on global image similarities and local features to image overlap prediction. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a vision Transformer backbone and establishing patch-to-patch correspondences without requiring expensive feature detection and matching. Our approach uses a voting mechanism to assess overlap scores for potential database images. It provides a nuanced image retrieval metric in challenging scenarios. Experimental results show that VOP leads to more accurate relative pose estimation and localization results on the retrieved image pairs than state-of-the-art base-lines on a number of large-scale, real-world indoor and outdoor benchmarks. The code is available at https://***/weitong8591/***.

关键词： Measurement Location awareness Visualization Accuracy Three-dimensional displays Pose estimation Image retrieval Focusing Transformers Visual place recognition

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Face Anonymization Made Simple

Face Anonymization Made Simple

引用

ieee workshop on applications of computer vision (WACV)

作者： Han-Wei Kung Tuomas Varanka Sanjay Saha Terence Sim Nicu Sebe University of Trento University of Oulu National University of Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast, our approach uses diffusion models with only a reconstruction loss, eliminating the need for facial landmarks or masks while still producing images with intricate, fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization, facial attribute preservation, and image quality. beyond its primary function of anonymization, our model can also perform face swapping tasks by incorporating an additional facial image as input, demonstrating its versatility and potential for diverse applications. Our code and models are available at https://***/hanweikung/face_anon_simple.

关键词： Image quality Data privacy computer vision Face recognition Computational modeling Diffusion models Information filtering Image reconstruction Facial features Information integrity

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RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis

RayGauss: Volumetric Gaussian-Based Ray Casting for Photorea...

引用

ieee workshop on applications of computer vision (WACV)

作者： Hugo Blanc Jean-Emmanuel Deschaud Alexis Paljic Centre for Robotics Mines Paris - PSL PSL University Paris France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Differentiable volumetric rendering-based methods made significant progress in novel view synthesis. On one hand, innovative methods have replaced the Neural Radiance Fields (NeRF) network with locally parame-terized structures, enabling high-quality renderings in a reasonable time. On the other hand, approaches have used differentiable splatting instead of NeRF's ray casting to optimize radiance fields rapidly using Gaussian kernels, allowing for fine adaptation to the scene. However, differentiable ray casting of irregularly spaced kernels has been scarcely explored, while splatting, despite enabling fast rendering times, is susceptible to clearly visible artifacts. Our work closes this gap by providing a physically consistent formulation of the emitted radiance c and density σ, decomposed with Gaussian functions associated with Spherical Gaussians/Harmonics for all-frequency colorimetric representation. We also introduce a method enabling differentiable ray casting of irregularly distributed Gaussians using an algorithm that integrates radiance fields slab by slab and leverages a BVH structure. This allows our approach to finely adapt to the scene while avoiding splatting artifacts. As a result, we achieve superior rendering quality compared to the state-of-the-art while maintaining reasonable training times and achieving inference speeds of 25 FPS on the Blender dataset. Project page with videos and code: ***

关键词： Training Hands Casting computer vision Rendering (computer graphics) Neural radiance field Inference algorithms Slabs Kernel Videos

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Graph-Jigsaw Conditioned Diffusion Model for Skeleton-Based Video Anomaly Detection

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-Based ...

引用

ieee workshop on applications of computer vision (WACV)

作者： Ali Karami Thi Kieu Khanh Ho Narges Armanfard Department of Electrical and Computer Engineering McGill University Mila - Quebec AI Institute Montreal QC Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enables operators to promptly de-tect suspicious activities, thereby enhancing safety. Achieving this demands a comprehensive understanding of human motions, both at body and region levels, while also accounting for the wide variations of performing a single action. However, existing studies fail to simultaneously address these crucial properties. This paper introduces a novel, practical, and lightweight framework, namely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD. GiCiSAD consists of three novel modules: the Graph Attention-based Forecasting module to capture the spatio-temporal dependencies inherent in the data, the Graph-level Jigsaw Puzzle Maker module to distinguish subtle region-level discrepancies between normal and abnormal motions, and the Graph-based Conditional Diffusion model to generate a wide spectrum of human motions. Extensive experiments on four widely used skeleton-based video datasets show that GiCiSAD outperforms existing methods with significantly fewer training parameters, establishing it as the new state-of-the-art.

关键词： Training computer vision Computational modeling Dynamics Benchmark testing Diffusion models Safety Forecasting Anomaly detection

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HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning

HeightMapNet: Explicit Height Modeling for End-to-End HD Map...

引用

ieee workshop on applications of computer vision (WACV)

作者： Wenzhao Qiu Shanmin Pang Hao Zhang Jianwu Fang Jianru Xue Xi'an Jiaotong University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advances in high-definition (HD) map construction from surround-view images have highlighted their costeffectiveness in deployment. However, prevailing techniques often fall short in accurately extracting and utilizing road features, as well as in the implementation of view transformation. In response, we introduce HeightMap-Net, a novel framework that establishes a dynamic relationship between image features and road surface height distributions. By integrating height priors, our approach refines the accuracy of Bird's-Eye-View (BEV) features beyond conventional methods. HeightMapNet also introduces a foreground-background separation network that sharply distinguishes between critical road elements and extraneous background components, enabling precise focus on detailed road micro-features. Additionally, our method leverages multi-scale features within the BEV space, optimally utilizing spatial geometric information to boost model performance. HeightMapNet has shown exceptional results on the challenging nuScenes and Argoverse 2 datasets, outperforming several widely recognized approaches. The code will be available at https://***/adasfag/HeightMapNet/.

关键词： computer vision Codes Accuracy Roads Feature extraction

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A Semantically Impactful Image Manipulation Dataset: Characterizing Image Manipulations Using Semantic Significance

A Semantically Impactful Image Manipulation Dataset: Charact...

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ieee workshop on applications of computer vision (WACV)

作者： Yuwei Chen Ming-Ching Chang Mattias Kirchner Zhenfei Zhang Xin Li Arslan Basharat Anthony Hoogs University at Albany Albany NY Kitware Clifton Park NY

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We investigate how to characterize semantic significance (SS) in detecting image manipulations (IMD) for media forensics. We introduce the Characterization of Seman-tic Impact for IMD (CSI-IMD) dataset, which focuses on localizing and evaluating the semantic impact of image manipulations to counter advanced generative techniques. Our evaluation of 10 state-of-the-art IMD and localization methods on CSI-IMD reveals key insights. Unlike existing datasets, CSI-IMD provides detailed semantic annotations beyond traditional manipulation masks, aiding in the development of new defensive strategies. The dataset features manipulations from advanced generation methods, offering various levels of semantic significance. It is divided into two parts: a gold-standard set of 1,000 manu-ally annotated manipulations with high-quality control, and an extended set of 500,000 automated manipulations for large-scale training and analysis. We also propose a new SS-focused task to assess the impact of semantically targeted manipulations. Our experiments show that current IMD methods struggle with manipulations created using stable diffusion, with TruFor and Cat-Net performing the best among those tested. The CSI-IMD dataset will become available at https://***/csiimd/csiimd.

关键词： Location awareness Training computer vision Annotations Semantics Digital forensics Media Software development management

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PRoGS: Progressive Rendering of Gaussian Splats

PRoGS: Progressive Rendering of Gaussian Splats

引用

ieee workshop on applications of computer vision (WACV)

作者： Brent Zoomers Maarten Wijnants Ivan Molenaers Joni Vanherck Jeroen Put Nick Michiels Hasselt University - Flanders Make - Digital Future Lab Diepenbeek Belgium

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Over the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.

关键词： Measurement computer vision Three-dimensional displays Accuracy Memory management Loading Focusing Bandwidth Rendering (computer graphics)

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FDS: Feedback-Guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization

FDS: Feedback-Guided Domain Synthesis with Multi-Source Cond...

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ieee workshop on applications of computer vision (WACV)

作者： Ali Bahri Mehrdad Noori Gustavo A. Vargas Hakim Ismail Ben Ayed Milad Cheraghalikhani David Osowiechi Christian Desrosiers Moslem Yazdanpanah ÉTS Montreal International Laboratory on Learning Systems (ILLS) Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Domain Generalization techniques aim to enhance model robustness by simulating novel data distributions during training, typically through various augmentation or stylization strategies. However, these methods frequently suffer from limited control over the diversity of generated images and lack assurance that these images span distinct distributions. To address these challenges, we propose FDS, Feedback-guided Domain Synthesis, a novel strategy that employs diffusion models to synthesize novel, pseudo-domains by training a single model on all source domains and performing domain mixing based on learned features. By incorporating images that pose classification challenges to models trained on original samples, alongside the original dataset, we ensure the generation of a training set that spans a broad distribution spectrum. Our comprehensive evaluations demonstrate that this methodology sets new benchmarks in domain generalization performance across a range of challenging datasets, effectively managing diverse types of domain shifts. The code can be found at: https://***/Mehrdad-Noori/FDS

关键词： Training Interpolation Extrapolation computer vision Codes Filtering Benchmark testing Diffusion models Robustness Data models

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Multispectral Object Detection Enhanced by Cross-Modal Information Complementary and Cosine Similarity Channel Resampling Modules

Multispectral Object Detection Enhanced by Cross-Modal Infor...

引用

ieee workshop on applications of computer vision (WACV)

作者： Junbo Jang Chanyeong Park Heegwang Kim Jiyoon Lee Joonki Paik Chung-Ang University Republic of Korea

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Images obtained from different modalities can effectively enhance the accuracy and reliability of the detection model by complementing specialized information from visible (RGB) and infrared (IR) images. However, integrating information from multiple modalities faces the following challenges: 1) distinct characteristics of RGB and IR images lead to the problem of modality imbalance, 2) fusing multimodal information can greatly affect the detection accuracy, as some of the unique information provided by each modality is lost during the integration process, and 3) RGB and IR images are fused while preserving the noise of each modality. To address these issues, we propose a novel multi spectral object detection network which contains two main components; 1) Cross-modal Information Complementary (CIC) module, and 2) Cosine Similarity Channel Resampling (CSCR) module. The proposed method addresses the modality imbalance problem and efficiently fuses RGB and IR images in the feature level. Extensive experimental results on LLVIP, FLIR, $M^{3}FD$ , VEDAI and KAIST benchmark datasets, verify the effectiveness and generalization performance of the proposed multispectral object detection network compared with other state-of-the-art methods.

关键词： computer vision Adaptation models Accuracy Fuses Computational modeling Noise Object detection Benchmark testing Reliability Faces

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