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共 20950 条记录，以下是601-610 订阅

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FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model...

引用

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

作者： Yang, Julia Barnett, Alina Jade Donnelly, Jon Kishore, Satvik Fang, Jerry Schwartz, Fides Regina Chen, Chaofan Lo, Joseph Y. Rudin, Cynthia Duke Univ Durham NC 27708 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Univ Maine Orono ME USA

ISBN: (纸本)9798350365474

Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-stakes environments, uninterpretable ("black box") deep learning models are unsuitable and there is a call in these fields to make interpretable models. Recent work in interpretable computer vision provides transparency to these formerly black boxes by utilizing prototypes for case-based explanations, achieving high accuracy in applications including mammography. However, these models struggle with precise feature localization, reasoning on large portions of an image when only a small part is relevant. This paper addresses this gap by proposing a novel multi-scale interpretable deep learning model for mammographic mass margin classification. Our contribution not only offers an interpretable model with reasoning aligned with radiologist practices, but also provides a general architecture for computer vision with user-configurable prototypes from coarse-to fine-grained prototypes.

关键词： breast cancer cancer computer vision deep learning interpretability interpretable machine learning machine learning mammography medical imaging neural networks

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1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense P...

引用

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

作者： Yin, Dongshuo Yang, Yiran Wang, Zhechao Yu, Hongfeng Wei, Kaiwen Sun, Xian Chinese Acad Sci Aerosp Informat Res Inst Key Lab Network Informat Syst Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In this work, we propose LoRand, a method for fine-tuning large-scale vision models with a better trade-off between task performance and the number of trainable parameters. LoRand generates tiny adapter structures with low-rank synthesis while keeping the original backbone parameters fixed, resulting in high parameter sharing. To demonstrate LoRand's effectiveness, we implement extensive experiments on object detection, semantic segmentation, and instance segmentation tasks. By only training a small percentage (1% to 3%) of the pre-trained backbone parameters, LoRand achieves comparable performance to standard fine-tuning on COCO and ADE20K and outperforms fine-tuning in low-resource PASCAL VOC dataset.

关键词： Efficient and scalable vision

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Towards Professional Level Crowd Annotation of Expert Domain Data

Towards Professional Level Crowd Annotation of Expert Domain...

引用

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

作者： Wang, Pei Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9798350301298

Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A new approach, based on semi-supervised learning (SSL) and denoted as SSL with human filtering (SSL-HF) is proposed. It is a human-in-the-loop SSL method, where crowd-source workers act as filters of pseudo-labels, replacing the unreliable confidence thresholding used by state-of-the-art SSL methods. To enable annotation by non-experts, classes are specified implicitly, via positive and negative sets of examples and augmented with deliberative explanations, which highlight regions of class ambiguity. In this way, SSL-HF leverages the strong low-shot learning and confidence estimation ability of humans to create an intuitive but effective labeling experience. Experiments show that SSL-HF significantly outperforms various alternative approaches in several benchmarks.

关键词： Efficient and scalable vision

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Confidence-Aware RGB-D Face recognition via Virtual Depth Synthesis

Confidence-Aware RGB-D Face Recognition via Virtual Depth Sy...

引用

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

作者： Chen, Zijian Wang, Mei Deng, Weihong Shi, Hongzhi Wen, Dongchao Zhang, Yingjie Cui, Xingchen Zhao, Jian Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350365474

2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. However, collecting sufficient paired RGB-D training data is expensive and time-consuming, hindering wide deployment. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining. To seamlessly integrate the two distinct networks and harness the complementary benefits of RGB and depth information for improved accuracy, we propose an innovative Adaptive Confidence Weighting (ACW). This mechanism is designed to learn confidence estimates for each modality to achieve modality fusion at the score level. Our method is simple and lightweight, only requiring ACW training beyond the backbone models. Experiments on multiple public RGB-D face recognition benchmarks demonstrate state-of-the-art performance surpassing previous methods based on depth estimation and feature fusion, validating the efficacy of our approach.

关键词： domain-independent pre-training modality fusion RGB-D face recognition

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LightPainter: Interactive Portrait Relighting with Freehand Scribble

LightPainter: Interactive Portrait Relighting with Freehand ...

引用

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

作者： Mei, Yiqun Zhang, He Zhang, Xuaner Zhang, Jianming Shu, Zhixin Wang, Yilin Wei, Zijun Yan, Shi Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

关键词： vision + graphics

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MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis

MoFusion: A Framework for Denoising-Diffusion-based Motion S...

引用

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

作者： Dabral, Rishabh Mughal, Muhammad Hamza Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350301298

Conventional methods for human motion synthesis have either been deterministic or have had to struggle with the trade-off between motion diversity vs motion quality. In response to these limitations, we introduce MoFusion, i.e., a new denoising-diffusion-based framework for high-quality conditional human motion synthesis that can synthesise long, temporally plausible, and semantically accurate motions based on a range of conditioning contexts (such as music and text). We also present ways to introduce well-known kinematic losses for motion plausibility within the motion-diffusion framework through our scheduled weighting strategy. The learned latent space can be used for several interactive motion-editing applications like in-betweening, seed-conditioning, and text-based editing, thus, providing crucial abilities for virtual-character animation and robotics. Through comprehensive quantitative evaluations and a perceptual user study, we demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature. We urge the reader to watch our supplementary video at https://***/projects/MoFusion/.

关键词： language reasoning vision

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

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

作者： Senadeera, Damith Chamalke Yang, Xiaoyun Kollias, Dimitrios Slabaugh, Gregory Queen Mary Univ London Sch Elect Engn & Comp Sci London England Queen Marys Digital Environm Res Inst DERI London England Remark AI UK Ltd London England

ISBN: (纸本)9798350365474

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatio-temporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at (1).

关键词： computer vision Cropping Deep Learning Efficient Additive Attention UniFormerV2 Video Analytics Violence Detection

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Iterative vision-and-Language Navigation

Iterative Vision-and-Language Navigation

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

作者： Krantz, Jacob Banerjee, Shurjo Zhu, Wang Corso, Jason Anderson, Peter Lee, Stefan Thomason, Jesse Oregon State Univ Corvallis OR 97331 USA Univ Michigan Ann Arbor MI USA Univ Southern Calif Los Angeles CA USA Google Res Sunnyvale CA USA

ISBN: (纸本)9798350301298

We present Iterative vision-and-Language Navigation (IVLN), a paradigm for evaluating language-guided agents navigating in a persistent environment over time. Existing vision-and-Language Navigation (VLN) benchmarks erase the agent's memory at the beginning of every episode, testing the ability to perform cold-start navigation with no prior information. However, deployed robots occupy the same environment for long periods of time. The IVLN paradigm addresses this disparity by training and evaluating VLN agents that maintain memory across tours of scenes that consist of up to 100 ordered instruction-following Room-to-Room (R2R) episodes, each defined by an individual language instruction and a target path. We present discrete and continuous Iterative Room-to-Room (IR2R) benchmarks comprising about 400 tours each in 80 indoor scenes. We find that extending the implicit memory of high-performing transformer VLN agents is not sufficient for IVLN, but agents that build maps can benefit from environment persistence, motivating a renewed focus on map-building agents in VLN.

关键词： language reasoning vision

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Once for Both: Single Stage of Importance and Sparsity Search for vision Transformer Compression

Once for Both: Single Stage of Importance and Sparsity Searc...

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

作者： Ye, Hancheng Yu, Chong Ye, Peng Xia, Renqiu Tang, Yansong Lu, Jiwen Chen, Tao Zhang, Bo Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions, thus causing high search costs for VTC. In this work, for the first time, we investigate how to integrate the evaluations of importance and sparsity scores into a single stage, searching the optimal subnets in an efficient manner. Specifically, we present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores, termed Once for Both (OFB), for VTC. First, a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-andefficient search for the most important subnet. Finally, Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process, which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various vision Transformer architectures, meanwhile promoting search efficiency significantly, e.g., costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

关键词： Image compression

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Revisiting Self-Similarity: Structural Embedding for Image Retrieval

Revisiting Self-Similarity: Structural Embedding for Image R...

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

作者： Lee, Seongwon Lee, Suhyeon Seong, Hongje Kim, Euntai Yonsci Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9798350301298

Despite advances in global image representation, existing image retrieval approaches rarely consider geometric structure during the global retrieval stage. In this work, we revisit the conventional self-similarity descriptor from a convolutional perspective, to encode both the visual and structural cues of the image to global image representation. Our proposed network, named Structural Embedding Network (SENet), captures the internal structure of the images and gradually compresses them into dense self-similarity descriptors while learning diverse structures from various images. These self-similarity descriptors and original image features are fused and then pooled into global embedding, so that global embedding can represent both geometric and visual cues of the image. Along with this novel structural embedding, our proposed network sets new state-of-the-art performances on several image retrieval benchmarks, convincing its robustness to look-alike distractors. The code and models are available: https://***/sungonce/SENet.

关键词： detection recognition: Categorization retrieval

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