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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是111-120 订阅

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InVERGe: Intelligent Visual Encoder for Bridging Modalities in Report Generation

InVERGe: Intelligent Visual Encoder for Bridging Modalities ...

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

作者： Deria, Ankan Kumar, Komal Chakraborty, Snehashis Mahapatra, Dwarikanath Roy, Sudipta Jio Inst Artificial Intelligence & Data Sci Navi Mumbai 410206 India Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350365474

Medical image captioning plays an important role in modern healthcare, improving clinical report generation and aiding radiologists in detecting abnormalities and reducing misdiagnosis. The complex visual and textual data biases make this task more challenging. Recent advancements in transformer-based models have significantly improved the generation of radiology reports from medical images. However, these models require substantial computational resources for training and have been observed to produce unnatural language outputs when trained solely on raw image-text pairs. Our aim is to generate more detailed reports specific to images and to explain the reasoning behind the generated text through image-text alignment. Given the high computational demands of end-to-end model training, we introduce a two-step training methodology with an Intelligent Visual Encoder for Bridging Modalities in Report Generation (InVERGe) model. This model incorporates a lightweight transformer known as the Cross-Modal Query Fusion Layer (CMQFL), which utilizes the output from a frozen encoder to identify the most relevant text-grounded image embedding. This layer bridges the gap between the encoder and decoder, significantly reducing the workload on the decoder and enhancing the alignment between vision and language. Our experimental results, conducted using the MIMIC-CXR, Indiana University chest X-ray images, and CDD-CESM breast images datasets, demonstrate the effectiveness of our approach. Code: https://***/labsroy007/InVERGe

关键词： computer vision Deep Learning Large Language Model Medical Imaging Report Generation

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

引用

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

作者： Perez, Fabian Rueda-Chacon, Hoover Univ Ind Santander Bucaramanga Colombia

ISBN: (纸本)9798350365474

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/ Spectral-material-segmentation

关键词： Convolutional Neural Networks Hyperspectral Images LiDAR Material recognition Material Segmentation Point Clouds RGB-D

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FineRehab: A Multi-modality and Multi-task Dataset for Rehabilitation Analysis

FineRehab: A Multi-modality and Multi-task Dataset for Rehab...

引用

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

作者： Li, Jianwei Xue, Jun Cao, Rui Du, Xiaoxia Mo, Siyu Ran, Kehao Zhang, Zeyan Beijing Sport Univ Sch Sports Engn Beijing Peoples R China Rehabil Res Ctr Dept Neurorehabil Beijing Peoples R China

ISBN: (纸本)9798350365474

The assessment of rehabilitation exercises for neurological and musculoskeletal disorders are crucial for recovery. Traditionally, assessment methods have been subjective, with inherent uncertainty and limitations. This paper introduces a novel multi-modality dataset named FineRehab to prompt the study of rehabilitation movement analysis, leveraging advancements in sensor technology and artificial intelligence. FineRehab collects 16 actions from 50 participants, including both patients with musculoskeletal disorders and healthy individuals, and consists of 4,215 action samples captured by two Kinect cameras and 17 IMUs. To benchmark FineRehab, we present a reliable approach to analyze rehabilitation exercises, and make experiments to evaluate the comprehensive movement quality from across multi-dimensions. Comparative experimental analyses have verified the validity of our dataset in distinguishing between the movement of the normal population and patients, which can offer a quantifiable basis for personalized rehabilitation feedback. The introduction of FineRehab will encourage researchers to apply, develop and adapt various methods for rehabilitation exercise analysis.

关键词： Action quality assessment Action recognition Dataset Rehabilitation exercise

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

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

作者： Hukkelas, Hakon Lindseth, Frank Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway

ISBN: (纸本)9798350302493

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

关键词： computer vision

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

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

作者： Zhang, Wenjin Li, Keyi Yang, Sen Yuan, Sifan Marsic, Ivan Sippel, Genevieve J. Kim, Mary S. Burd, Randall S. Rutgers State Univ New Brunswick NJ 08901 USA Waymo Mountain View CA USA Childrens Natl Hosp Washington DC USA

ISBN: (纸本)9798350365474

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： medical activity recognition self-supervised learning video understanding

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

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

作者： Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are finetuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to finetune the ViT-B variants of VFMs with a 16 x 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page(1).

关键词： benchmark semantic segmentation vision foundation models

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POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any...

引用

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

作者： Fan, Zhiwen Pan, Panwang Wang, Peihao Jiang, Yifan Xu, Dejia Wang, Zhangyang ByteDance Beijing Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied agents and downstream 3D vision tasks. These limitations mainly come from the necessity of 3D models, closed-category detection, and a large number of densely annotated support views. To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE). The proposed approach POPE enables zero-shot 6DoF object pose estimation for any target object in any scene, while only a single reference is adopted as the support view. To achieve this, POPE lever-ages the power of the pre-trained large-scale 2D foundation model, employs a framework with hierarchical feature representation and 3D geometry principles. Moreover, it estimates the relative camera pose between object prompts and the target object in new views, enabling both two-view and multi-view 6DoF pose estimation tasks. Comprehensive experimental results demonstrate that POPE exhibits unrivaled robust performance in zero-shot settings, by achieving a significant reduction in the averaged Median Pose Error by 52.38% and 50.47% on the LINEMOD [22] and OnePose [54] datasets, respectively. We also conduct more challenging testings in causally captured images (see Figure 1), which further demonstrates the robustness of POPE.

关键词： Object recognition

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Video Based Computational Coding of Movement Anomalies in ASD Children

Video Based Computational Coding of Movement Anomalies in AS...

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

作者： Singh, Priya Pathak, Abhishek Ganai, Umer Jon Bhushan, Braj Subramanian, Venkatesh K. TCS Res Mumbai Maharashtra India Indian Inst Technol Kanpur Kanpur India Airbase Labs India Pvt Ltd Bengaluru India

ISBN: (纸本)9798350365474

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Early detection and diagnosis are instrumental in early intervention, yet diagnosis often remains delayed due to the limited availability of clinical practitioners and specialists. We propose a computer vision and Machine Learning based novel framework for quantitative screening of autism spectrum disorder (ASD). This is aimed to minimize the need for trained professionals at the initial stage but not substitute for it. We designed simple activities in consultation with ASD clinical psychologists and therapists for children in the 3-7 years age group that could be performed in their natural environment (home). The temporal features extracted from these activities encode the behavioral differences between Autism Spectrum Disorder (ASD) and Typically Developing (TD) control groups. Due to the unavailability of a public dataset of children performing the designed task, we created our own video dataset of 210 videos taken in unconstrained natural settings. The dataset was collected from a single RGB camera. The proposed vision and learning-based algorithms extract features from the collected data for a comprehensive set of indicators including the visual attention span, name-calling response, neck pose of the subjects, gross motor movement and establish a parametrized automated protocol for early detection without the need to take the subjects out of their natural daily environment. This forestalls the possibility of misperformance by the subject out of nervousness due to unfamiliar surroundings. Results show that our ASD screening methodology can achieve superior performance compared to the single phenotype approaches, and thus has a prognostic value that could be helpful for both clinical and research applications.

关键词： Autism Spectrum Disorder computer vision Early detection of ASD Machine Learning

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

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

作者： Prabhune, Omkar Chen, Tianen Kim, Younghyun Purdue Univ W Lafayette IN 47907 USA Univ Wisconsin Madison WI 53706 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350365474

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication overhead. In this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a contentaware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： AI on edge computation offloading content-aware compression content-aware offloading deep learning edge computing edge ML efficient deep learning efficient ML embedded vision face identification low-latency deep learning low-power ML ml system tinyML

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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

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

作者： Sarkar, Sreetama Kundu, Souvik Zheng, Kai Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA Intel Labs San Diego CA USA

ISBN: (纸本)9798350365474

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4x and compute cost by up to 2x while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： on-device training token pruning vision transformer

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